Machine learning Books
Springer Nature Switzerland AG Machine Learning for Text
Book SynopsisThis second edition textbook covers a coherently organized framework for text analytics, which integrates material drawn from the intersecting topics of information retrieval, machine learning, and natural language processing. Particular importance is placed on deep learning methods. The chapters of this book span three broad categories:1. Basic algorithms: Chapters 1 through 7 discuss the classical algorithms for text analytics such as preprocessing, similarity computation, topic modeling, matrix factorization, clustering, classification, regression, and ensemble analysis.2. Domain-sensitive learning and information retrieval: Chapters 8 and 9 discuss learning models in heterogeneous settings such as a combination of text with multimedia or Web links. The problem of information retrieval and Web search is also discussed in the context of its relationship with ranking and machine learning methods. 3. Natural language processing: Chapters 10 through 16 discuss various sequence-centric and natural language applications, such as feature engineering, neural language models, deep learning, transformers, pre-trained language models, text summarization, information extraction, knowledge graphs, question answering, opinion mining, text segmentation, and event detection. Compared to the first edition, this second edition textbook (which targets mostly advanced level students majoring in computer science and math) has substantially more material on deep learning and natural language processing. Significant focus is placed on topics like transformers, pre-trained language models, knowledge graphs, and question answering.Table of Contents1 An Introduction to Text Analytics.- 2 Text Preparation and Similarity Computation.- 3 Matrix Factorization and Topic Modeling.- 4 Text Clustering.- 5 Text Classification: Basic Models.- 6 Linear Models for Classification and Regression.- 7 Classifier Performance and Evaluation.- 8 Joint Text Mining with Heterogeneous Data.- 9 Information Retrieval and Search Engines.- 10 Language Modeling and Deep Learning.- 11 Attention Mechanisms and Transformers.- 12 Text Summarization.- 13 Information Extraction and Knowledge Graphs.- 14 Question Answering.- 15 Opinion Mining and Sentiment Analysis.- 16 Text Segmentation and Event Detection.
£44.99
Springer Nature Switzerland AG Data Analytics in e-Learning: Approaches and
Book SynopsisThis book focuses on research and development aspects of building data analytics workflows that address various challenges of e-learning applications. This book represents a guideline for building a data analysis workflow from scratch. Each chapter presents a step of the entire workflow, starting from an available dataset and continuing with building interpretable models, enhancing models, and tackling aspects of evaluating engagement and usability. The related work shows that many papers have focused on machine learning usage and advancement within e-learning systems. However, limited discussions have been found on presenting a detailed complete roadmap from the raw dataset up to the engagement and usability issues. Practical examples and guidelines are provided for designing and implementing new algorithms that address specific problems or functionalities. This roadmap represents a potential resource for various advances of researchers and practitioners in educational data mining and learning analytics.Table of ContentsIntroduction to Data Analytics in e-Learning
£116.99
Springer Nature Switzerland AG Adversarial Machine Learning: Attack Surfaces, Defence Mechanisms, Learning Theories in Artificial Intelligence
Book SynopsisA critical challenge in deep learning is the vulnerability of deep learning networks to security attacks from intelligent cyber adversaries. Even innocuous perturbations to the training data can be used to manipulate the behaviour of deep networks in unintended ways. In this book, we review the latest developments in adversarial attack technologies in computer vision; natural language processing; and cybersecurity with regard to multidimensional, textual and image data, sequence data, and temporal data. In turn, we assess the robustness properties of deep learning networks to produce a taxonomy of adversarial examples that characterises the security of learning systems using game theoretical adversarial deep learning algorithms. The state-of-the-art in adversarial perturbation-based privacy protection mechanisms is also reviewed. We propose new adversary types for game theoretical objectives in non-stationary computational learning environments. Proper quantification of the hypothesis set in the decision problems of our research leads to various functional problems, oracular problems, sampling tasks, and optimization problems. We also address the defence mechanisms currently available for deep learning models deployed in real-world environments. The learning theories used in these defence mechanisms concern data representations, feature manipulations, misclassifications costs, sensitivity landscapes, distributional robustness, and complexity classes of the adversarial deep learning algorithms and their applications. In closing, we propose future research directions in adversarial deep learning applications for resilient learning system design and review formalized learning assumptions concerning the attack surfaces and robustness characteristics of artificial intelligence applications so as to deconstruct the contemporary adversarial deep learning designs. Given its scope, the book will be of interest to Adversarial Machine Learning practitioners and Adversarial Artificial Intelligence researchers whose work involves the design and application of Adversarial Deep Learning.Table of ContentsAdversarial Machine Learning.- Adversarial Deep Learning.- Security and Privacy in Adversarial Learning.- Game-Theoretical Attacks with Adversarial Deep Learning Models.- Physical Attacks in the Real World.- Adversarial Defense Mechanisms.- Adversarial Learning for Privacy Preservation.
£125.99
Springer International Publishing AG Artificial Intelligence/Machine Learning in
Book SynopsisThis book includes detailed explanations of the underlying technologies and concepts used in Artificial Intelligence (AI) and Machine Learning (ML) in the context of nuclear medicine and hybrid imaging. A diverse team of authors, including pioneers in the field and respected experts from leading international institutions, share their insights, opinions and outlooks on this exciting topic.A wide range of clinical applications are discussed, from brain applications to body indications, as well as the applicability of AI and ML for cardio-vascular conditions. The book also considers the potential impact of theranostics. To balance the technology-heavy and disease-specific applications, it also discusses ethical / legal issues, economic realities and the human factor, the physician. Though this discussion is not based on research and outcomes, it provides important insights into the ramifications of how AI and ML could transform Nuclear Medicine and Hybrid Imaging practice.As the first work highlighting the role of these concepts specifically in this field, rather than for medical imaging in general, this book offers a valuable resource for Nuclear Medicine Physicians, Radiologists, Physicists, Medical Imaging Administrators and Nuclear Medicine Technologists alike.Table of ContentsPART I: INTRODUCTION Editorial: Benefits and challenges of AI/ML in hybrid imaging and molecular imaging P. Veit-Haibach and Ken Herrmann PART II: TECHNOLOGY Role and influence of AI/ML in healthcare and specifically hybrid imaging and molecular imaging (Benjamin L. Franc and Guido Davidzon) Radiomics, Radiogenomics, AI, Deep-Learning and Machine Learning - Definitions and Imaging Applications (Jens Kleesiek, Germany) Radiomics in Nuclear Medicine - Robustness, Reproducibility, Standardisation (Reza Rezai, Toronto) Evolution of AI/ML in molecular imaging – we did this before AI based applications in Hybrid Imaging – how to build smart and truly multi-parametric decision models Basic principles of neural networks: types, applications and “usefulness” for molecular imaging (Josh Kaggie, Cambridge, UK, Chitresh Bhushan, GE, Niskayuna, NY, US and Dawei Gui, GE, Waukesha, Wi, US) PART III CLINICAL APPLICATIONS Imaging biomarkers and their meaning for molecular imaging (Angel Alberich Bayarri, Valencia, Spain) Integration of AI/ML in Clinical Routine in Molecular Imaging Structured reporting with AI/ML in Molecular Imaging in Theranostics Imaging biobanks for molecular imaging – how to integrate ML/AI into our databases Angel Alberich Bayarri, Valencia, Spain) AI/ML Imaging applications in head and neck diseases (neuro/onco) (Mijin Jun, Seoul, Korea for neurodegenerative applications) AI/ML Imaging applications in body oncology (Robert Seifert et al, Muenster, Germany) AI/ML imaging applications in cardiac imaging (Piotr Slomka, LA, US) AI/ML Imaging applications in therapy follow up and therapy decision support PART IV: Impact of A.I. and ML on Molecular Imaging and Theranostics AI/ML will help to improve Molecular Imaging as well as the Molecular Therapy/Theranostic – what are the biggest advantages for Imaging and Therapy?. (Benjamin L. Franc and ?) Why imaging data is not enough – AI based integration of imaging and clinical data Legal and ethical issues in AI/ML – why does the patient does not own his /her data? (Prainsack, Vienna) The role of A.I./ML for clinical trials in molecular imaging Show me the money – investor landscape in AI/ML in molecular imaging and therapy Physician centered imaging interpretation is dying out - why should I be a radiologist/nuclear medicine physician or how do we attract the smartest people to our field (Roland Hustinx, Liege, Belgium) Advantages and risks of A.I./ML for Molecular Imaging Specialists – what do we need to learn to master the “beast”
£66.49
Springer International Publishing AG Intelligent Autonomous Robotics: A Robot Soccer Case Study
Book SynopsisRobotics technology has recently advanced to the point of being widely accessible for relatively low-budget research, as well as for graduate, undergraduate, and even secondary and primary school education. This lecture provides an example of how to productively use a cutting-edge advanced robotics platform for education and research by providing a detailed case study with the Sony AIBO robot, a vision-based legged robot. The case study used for this lecture is the UT Austin Villa RoboCup Four-Legged Team. This lecture describes both the development process and the technical details of its end result. The main contributions of this lecture are (i) a roadmap for new classes and research groups interested in intelligent autonomous robotics who are starting from scratch with a new robot, and (ii) documentation of the algorithms behind our own approach on the AIBOs with the goal of making them accessible for use on other vision-based and/or legged robot platforms.Table of ContentsIntroduction.- The Class.- Initial Behaviors.- Vision.- Movement.- Fall Detection.- Kicking.- Localization.- Communication.- General Architecture.- Global Map.- Behaviors.- Coordination.- Simulator.- UT Assist.- Conclusion.
£25.19
Springer International Publishing AG Representation Discovery using Harmonic Analysis
Book SynopsisRepresentations are at the heart of artificial intelligence (AI). This book is devoted to the problem of representation discovery: how can an intelligent system construct representations from its experience? Representation discovery re-parameterizes the state space - prior to the application of information retrieval, machine learning, or optimization techniques - facilitating later inference processes by constructing new task-specific bases adapted to the state space geometry. This book presents a general approach to representation discovery using the framework of harmonic analysis, in particular Fourier and wavelet analysis. Biometric compression methods, the compact disc, the computerized axial tomography (CAT) scanner in medicine, JPEG compression, and spectral analysis of time-series data are among the many applications of classical Fourier and wavelet analysis. A central goal of this book is to show that these analytical tools can be generalized from their usual setting in (infinite-dimensional) Euclidean spaces to discrete (finite-dimensional) spaces typically studied in many subfields of AI. Generalizing harmonic analysis to discrete spaces poses many challenges: a discrete representation of the space must be adaptively acquired; basis functions are not pre-defined, but rather must be constructed. Algorithms for efficiently computing and representing bases require dealing with the curse of dimensionality. However, the benefits can outweigh the costs, since the extracted basis functions outperform parametric bases as they often reflect the irregular shape of a particular state space. Case studies from computer graphics, information retrieval, machine learning, and state space planning are used to illustrate the benefits of the proposed framework, and the challenges that remain to be addressed. Representation discovery is an actively developing field, and the author hopes this book will encourage other researchers to explore this exciting area of research. Table of Contents: Overview / Vector Spaces / Fourier Bases on Graphs / Multiscale Bases on Graphs / Scaling to Large Spaces / Case Study: State-Space Planning / Case Study: Computer Graphics / Case Study: Natural Language / Future DirectionsTable of ContentsOverview.- Vector Spaces.- Fourier Bases on Graphs.- Multiscale Bases on Graphs.- Scaling to Large Spaces.- Case Study: State-Space Planning.- Case Study: Computer Graphics.- Case Study: Natural Language.- Future Directions.
£25.19
Springer International Publishing AG Introduction to Semi-Supervised Learning
Book SynopsisSemi-supervised learning is a learning paradigm concerned with the study of how computers and natural systems such as humans learn in the presence of both labeled and unlabeled data. Traditionally, learning has been studied either in the unsupervised paradigm (e.g., clustering, outlier detection) where all the data are unlabeled, or in the supervised paradigm (e.g., classification, regression) where all the data are labeled. The goal of semi-supervised learning is to understand how combining labeled and unlabeled data may change the learning behavior, and design algorithms that take advantage of such a combination. Semi-supervised learning is of great interest in machine learning and data mining because it can use readily available unlabeled data to improve supervised learning tasks when the labeled data are scarce or expensive. Semi-supervised learning also shows potential as a quantitative tool to understand human category learning, where most of the input is self-evidently unlabeled. In this introductory book, we present some popular semi-supervised learning models, including self-training, mixture models, co-training and multiview learning, graph-based methods, and semi-supervised support vector machines. For each model, we discuss its basic mathematical formulation. The success of semi-supervised learning depends critically on some underlying assumptions. We emphasize the assumptions made by each model and give counterexamples when appropriate to demonstrate the limitations of the different models. In addition, we discuss semi-supervised learning for cognitive psychology. Finally, we give a computational learning theoretic perspective on semi-supervised learning, and we conclude the book with a brief discussion of open questions in the field. Table of Contents: Introduction to Statistical Machine Learning / Overview of Semi-Supervised Learning / Mixture Models and EM / Co-Training / Graph-Based Semi-Supervised Learning / Semi-Supervised Support Vector Machines / Human Semi-Supervised Learning / Theory and OutlookTable of ContentsIntroduction to Statistical Machine Learning.- Overview of Semi-Supervised Learning.- Mixture Models and EM.- Co-Training.- Graph-Based Semi-Supervised Learning.- Semi-Supervised Support Vector Machines.- Human Semi-Supervised Learning.- Theory and Outlook.
£26.59
Springer International Publishing AG Markov Logic: An Interface Layer for Artificial Intelligence
Book SynopsisMost subfields of computer science have an interface layer via which applications communicate with the infrastructure, and this is key to their success (e.g., the Internet in networking, the relational model in databases, etc.). So far this interface layer has been missing in AI. First-order logic and probabilistic graphical models each have some of the necessary features, but a viable interface layer requires combining both. Markov logic is a powerful new language that accomplishes this by attaching weights to first-order formulas and treating them as templates for features of Markov random fields. Most statistical models in wide use are special cases of Markov logic, and first-order logic is its infinite-weight limit. Inference algorithms for Markov logic combine ideas from satisfiability, Markov chain Monte Carlo, belief propagation, and resolution. Learning algorithms make use of conditional likelihood, convex optimization, and inductive logic programming. Markov logic has been successfully applied to problems in information extraction and integration, natural language processing, robot mapping, social networks, computational biology, and others, and is the basis of the open-source Alchemy system. Table of Contents: Introduction / Markov Logic / Inference / Learning / Extensions / Applications / ConclusionTable of ContentsIntroduction.- Markov Logic.- Inference.- Learning.- Extensions.- Applications.- Conclusion.
£25.19
Springer International Publishing AG Data Integration: The Relational Logic Approach
Book SynopsisData integration is a critical problem in our increasingly interconnected but inevitably heterogeneous world. There are numerous data sources available in organizational databases and on public information systems like the World Wide Web. Not surprisingly, the sources often use different vocabularies and different data structures, being created, as they are, by different people, at different times, for different purposes. The goal of data integration is to provide programmatic and human users with integrated access to multiple, heterogeneous data sources, giving each user the illusion of a single, homogeneous database designed for his or her specific need. The good news is that, in many cases, the data integration process can be automated. This book is an introduction to the problem of data integration and a rigorous account of one of the leading approaches to solving this problem, viz., the relational logic approach. Relational logic provides a theoretical framework for discussing data integration. Moreover, in many important cases, it provides algorithms for solving the problem in a computationally practical way. In many respects, relational logic does for data integration what relational algebra did for database theory several decades ago. A companion web site provides interactive demonstrations of the algorithms. Table of Contents: Preface / Interactive Edition / Introduction / Basic Concepts / Query Folding / Query Planning / Master Schema Management / Appendix / References / Index / Author Biography Don't have access? Recommend our Synthesis Digital Library to your library or purchase a personal subscription. Email info@morganclaypool.com for details.Table of ContentsPreface.- Interactive Edition.- Introduction.- Basic Concepts.- Query Folding.- Query Planning.- Master Schema Management.- Appendix.- References.- Index.- Author Biography.
£25.19
Springer International Publishing AG Visual Object Recognition
Book SynopsisThe visual recognition problem is central to computer vision research. From robotics to information retrieval, many desired applications demand the ability to identify and localize categories, places, and objects. This tutorial overviews computer vision algorithms for visual object recognition and image classification. We introduce primary representations and learning approaches, with an emphasis on recent advances in the field. The target audience consists of researchers or students working in AI, robotics, or vision who would like to understand what methods and representations are available for these problems. This lecture summarizes what is and isn't possible to do reliably today, and overviews key concepts that could be employed in systems requiring visual categorization. Table of Contents: Introduction / Overview: Recognition of Specific Objects / Local Features: Detection and Description / Matching Local Features / Geometric Verification of Matched Features / Example Systems: Specific-Object Recognition / Overview: Recognition of Generic Object Categories / Representations for Object Categories / Generic Object Detection: Finding and Scoring Candidates / Learning Generic Object Category Models / Example Systems: Generic Object Recognition / Other Considerations and Current Challenges / ConclusionsTable of ContentsIntroduction.- Overview: Recognition of Specific Objects.- Local Features: Detection and Description.- Matching Local Features.- Geometric Verification of Matched Features.- Example Systems: Specific-Object Recognition.- Overview: Recognition of Generic Object Categories.- Representations for Object Categories.- Generic Object Detection: Finding and Scoring Candidates.- Learning Generic Object Category Models.- Example Systems: Generic Object Recognition.- Other Considerations and Current Challenges.- Conclusions.
£25.19
Springer International Publishing AG Trading Agents
Book SynopsisAutomated trading in electronic markets is one of the most common and consequential applications of autonomous software agents. Design of effective trading strategies requires thorough understanding of how market mechanisms operate, and appreciation of strategic issues that commonly manifest in trading scenarios. Drawing on research in auction theory and artificial intelligence, this book presents core principles of strategic reasoning that apply to market situations. The author illustrates trading strategy choices through examples of concrete market environments, such as eBay, as well as abstract market models defined by configurations of auctions and traders. Techniques for addressing these choices constitute essential building blocks for the design of trading strategies for rich market applications. The lecture assumes no prior background in game theory or auction theory, or artificial intelligence. Table of Contents: Introduction / Example: Bidding on eBay / Auction Fundamentals / Continuous Double Auctions / Interdependent Markets / ConclusionTable of ContentsIntroduction.- Example: Bidding on eBay.- Auction Fundamentals.- Continuous Double Auctions.- Interdependent Markets.- Conclusion.
£25.19
Springer International Publishing AG Representations and Techniques for 3D Object Recognition and Scene Interpretation
Book SynopsisOne of the grand challenges of artificial intelligence is to enable computers to interpret 3D scenes and objects from imagery. This book organizes and introduces major concepts in 3D scene and object representation and inference from still images, with a focus on recent efforts to fuse models of geometry and perspective with statistical machine learning. The book is organized into three sections: (1) Interpretation of Physical Space; (2) Recognition of 3D Objects; and (3) Integrated 3D Scene Interpretation. The first discusses representations of spatial layout and techniques to interpret physical scenes from images. The second section introduces representations for 3D object categories that account for the intrinsically 3D nature of objects and provide robustness to change in viewpoints. The third section discusses strategies to unite inference of scene geometry and object pose and identity into a coherent scene interpretation. Each section broadly surveys important ideas from cognitive science and artificial intelligence research, organizes and discusses key concepts and techniques from recent work in computer vision, and describes a few sample approaches in detail. Newcomers to computer vision will benefit from introductions to basic concepts, such as single-view geometry and image classification, while experts and novices alike may find inspiration from the book's organization and discussion of the most recent ideas in 3D scene understanding and 3D object recognition. Specific topics include: mathematics of perspective geometry; visual elements of the physical scene, structural 3D scene representations; techniques and features for image and region categorization; historical perspective, computational models, and datasets and machine learning techniques for 3D object recognition; inferences of geometrical attributes of objects, such as size and pose; and probabilistic and feature-passing approaches for contextual reasoning about 3D objects and scenes. Table of Contents: Background on 3D Scene Models / Single-view Geometry / Modeling the Physical Scene / Categorizing Images and Regions / Examples of 3D Scene Interpretation / Background on 3D Recognition / Modeling 3D Objects / Recognizing and Understanding 3D Objects / Examples of 2D 1/2 Layout Models / Reasoning about Objects and Scenes / Cascades of Classifiers / Conclusion and Future DirectionsTable of ContentsBackground on 3D Scene Models.- Single-view Geometry.- Modeling the Physical Scene.- Categorizing Images and Regions.- Examples of 3D Scene Interpretation.- Background on 3D Recognition.- Modeling 3D Objects.- Recognizing and Understanding 3D Objects.- Examples of 2D 1/2 Layout Models.- Reasoning about Objects and Scenes.- Cascades of Classifiers.- Conclusion and Future Directions.
£26.99
Springer International Publishing AG Computational Aspects of Cooperative Game Theory
Book SynopsisCooperative game theory is a branch of (micro-)economics that studies the behavior of self-interested agents in strategic settings where binding agreements among agents are possible. Our aim in this book is to present a survey of work on the computational aspects of cooperative game theory. We begin by formally defining transferable utility games in characteristic function form, and introducing key solution concepts such as the core and the Shapley value. We then discuss two major issues that arise when considering such games from a computational perspective: identifying compact representations for games, and the closely related problem of efficiently computing solution concepts for games. We survey several formalisms for cooperative games that have been proposed in the literature, including, for example, cooperative games defined on networks, as well as general compact representation schemes such as MC-nets and skill games. As a detailed case study, we consider weighted voting games: a widely-used and practically important class of cooperative games that inherently have a natural compact representation. We investigate the complexity of solution concepts for such games, and generalizations of them. We briefly discuss games with non-transferable utility and partition function games. We then overview algorithms for identifying welfare-maximizing coalition structures and methods used by rational agents to form coalitions (even under uncertainty), including bargaining algorithms. We conclude by considering some developing topics, applications, and future research directions.Table of ContentsIntroduction.- Basic Concepts.- Representations and Algorithms.- Weighted Voting Games.- Beyond Characteristic Function Games.- Coalition Structure Formation.- Advanced Topics.
£26.99
Springer International Publishing AG Planning with Markov Decision Processes: An AI Perspective
Book SynopsisMarkov Decision Processes (MDPs) are widely popular in Artificial Intelligence for modeling sequential decision-making scenarios with probabilistic dynamics. They are the framework of choice when designing an intelligent agent that needs to act for long periods of time in an environment where its actions could have uncertain outcomes. MDPs are actively researched in two related subareas of AI, probabilistic planning and reinforcement learning. Probabilistic planning assumes known models for the agent's goals and domain dynamics, and focuses on determining how the agent should behave to achieve its objectives. On the other hand, reinforcement learning additionally learns these models based on the feedback the agent gets from the environment. This book provides a concise introduction to the use of MDPs for solving probabilistic planning problems, with an emphasis on the algorithmic perspective. It covers the whole spectrum of the field, from the basics to state-of-the-art optimal and approximation algorithms. We first describe the theoretical foundations of MDPs and the fundamental solution techniques for them. We then discuss modern optimal algorithms based on heuristic search and the use of structured representations. A major focus of the book is on the numerous approximation schemes for MDPs that have been developed in the AI literature. These include determinization-based approaches, sampling techniques, heuristic functions, dimensionality reduction, and hierarchical representations. Finally, we briefly introduce several extensions of the standard MDP classes that model and solve even more complex planning problems. Table of Contents: Introduction / MDPs / Fundamental Algorithms / Heuristic Search Algorithms / Symbolic Algorithms / Approximation Algorithms / Advanced NotesTable of ContentsIntroduction.- MDPs.- Fundamental Algorithms.- Heuristic Search Algorithms.- Symbolic Algorithms.- Approximation Algorithms.- Advanced Notes.
£26.99
Springer International Publishing AG Active Learning
Book SynopsisThe key idea behind active learning is that a machine learning algorithm can perform better with less training if it is allowed to choose the data from which it learns. An active learner may pose "queries," usually in the form of unlabeled data instances to be labeled by an "oracle" (e.g., a human annotator) that already understands the nature of the problem. This sort of approach is well-motivated in many modern machine learning and data mining applications, where unlabeled data may be abundant or easy to come by, but training labels are difficult, time-consuming, or expensive to obtain. This book is a general introduction to active learning. It outlines several scenarios in which queries might be formulated, and details many query selection algorithms which have been organized into four broad categories, or "query selection frameworks." We also touch on some of the theoretical foundations of active learning, and conclude with an overview of the strengths and weaknesses of these approaches in practice, including a summary of ongoing work to address these open challenges and opportunities. Table of Contents: Automating Inquiry / Uncertainty Sampling / Searching Through the Hypothesis Space / Minimizing Expected Error and Variance / Exploiting Structure in Data / Theory / Practical ConsiderationsTable of ContentsAutomating Inquiry.- Uncertainty Sampling.- Searching Through the Hypothesis Space.- Minimizing Expected Error and Variance.- Exploiting Structure in Data.- Theory.- Practical Considerations.
£26.59
Springer International Publishing AG Answer Set Solving in Practice
Book SynopsisAnswer Set Programming (ASP) is a declarative problem solving approach, initially tailored to modeling problems in the area of Knowledge Representation and Reasoning (KRR). More recently, its attractive combination of a rich yet simple modeling language with high-performance solving capacities has sparked interest in many other areas even beyond KRR. This book presents a practical introduction to ASP, aiming at using ASP languages and systems for solving application problems. Starting from the essential formal foundations, it introduces ASP's solving technology, modeling language and methodology, while illustrating the overall solving process by practical examples. Table of Contents: List of Figures / List of Tables / Motivation / Introduction / Basic modeling / Grounding / Characterizations / Solving / Systems / Advanced modeling / ConclusionsTable of ContentsList of Figures.- List of Tables.- Motivation.- Introduction.- Basic modeling.- Grounding.- Characterizations.- Solving.- Systems.- Advanced modeling.- Conclusions.
£37.85
Springer International Publishing AG Essential Principles for Autonomous Robotics
Book SynopsisFrom driving, flying, and swimming, to digging for unknown objects in space exploration, autonomous robots take on varied shapes and sizes. In part, autonomous robots are designed to perform tasks that are too dirty, dull, or dangerous for humans. With nontrivial autonomy and volition, they may soon claim their own place in human society. These robots will be our allies as we strive for understanding our natural and man-made environments and build positive synergies around us. Although we may never perfect replication of biological capabilities in robots, we must harness the inevitable emergence of robots that synchronizes with our own capacities to live, learn, and grow. This book is a snapshot of motivations and methodologies for our collective attempts to transform our lives and enable us to cohabit with robots that work with and for us. It reviews and guides the reader to seminal and continual developments that are the foundations for successful paradigms. It attempts to demystify the abilities and limitations of robots. It is a progress report on the continuing work that will fuel future endeavors. Table of Contents: Part I: Preliminaries/Agency, Motion, and Anatomy/Behaviors / Architectures / Affect/Sensors / Manipulators/Part II: Mobility/Potential Fields/Roadmaps / Reactive Navigation / Multi-Robot Mapping: Brick and Mortar Strategy / Part III: State of the Art / Multi-Robotics Phenomena / Human-Robot Interaction / Fuzzy Control / Decision Theory and Game Theory / Part IV: On the Horizon / Applications: Macro and Micro Robots / References / Author Biography / DiscussionTable of ContentsPart I: Preliminaries/Agency, Motion, and Anatomy.- Behaviors.- Architectures.- Affect/Sensors.- Manipulators/Part II: Mobility.- Potential Fields.- Roadmaps.- Reactive Navigation.- Multi-Robot Mapping: Brick and Mortar Strategy.- Part III: State of the Art.- Multi-Robotics Phenomena.- Human-Robot Interaction.- Fuzzy Control.- Decision Theory and Game Theory.- Part IV: On the Horizon.- Applications: Macro and Micro Robots.- References.- Author Biography.- Discussion.
£25.19
Springer International Publishing AG A Concise Introduction to Models and Methods for Automated Planning
Book SynopsisPlanning is the model-based approach to autonomous behavior where the agent behavior is derived automatically from a model of the actions, sensors, and goals. The main challenges in planning are computational as all models, whether featuring uncertainty and feedback or not, are intractable in the worst case when represented in compact form. In this book, we look at a variety of models used in AI planning, and at the methods that have been developed for solving them. The goal is to provide a modern and coherent view of planning that is precise, concise, and mostly self-contained, without being shallow. For this, we make no attempt at covering the whole variety of planning approaches, ideas, and applications, and focus on the essentials. The target audience of the book are students and researchers interested in autonomous behavior and planning from an AI, engineering, or cognitive science perspective. Table of Contents: Preface / Planning and Autonomous Behavior / Classical Planning: Full Information and Deterministic Actions / Classical Planning: Variations and Extensions / Beyond Classical Planning: Transformations / Planning with Sensing: Logical Models / MDP Planning: Stochastic Actions and Full Feedback / POMDP Planning: Stochastic Actions and Partial Feedback / Discussion / Bibliography / Author's BiographyTable of ContentsPreface.- Planning and Autonomous Behavior.- Classical Planning: Full Information and Deterministic Actions.- Classical Planning: Variations and Extensions.- Beyond Classical Planning: Transformations.- Planning with Sensing: Logical Models.- MDP Planning: Stochastic Actions and Full Feedback.- POMDP Planning: Stochastic Actions and Partial Feedback.- Discussion.- Bibliography.- Author's Biography.
£25.19
Springer International Publishing AG Introduction to Intelligent Systems in Traffic and Transportation
Book SynopsisUrban mobility is not only one of the pillars of modern economic systems, but also a key issue in the quest for equality of opportunity, once it can improve access to other services. Currently, however, there are a number of negative issues related to traffic, especially in mega-cities, such as economical issues (cost of opportunity caused by delays), environmental (externalities related to emissions of pollutants), and social (traffic accidents). Solutions to these issues are more and more closely tied to information and communication technology. Indeed, a search in the technical literature (using the keyword ``urban traffic" to filter out articles on data network traffic) retrieved the following number of articles (as of December 3, 2013): 9,443 (ACM Digital Library), 26,054 (Scopus), and 1,730,000 (Google Scholar). Moreover, articles listed in the ACM query relate to conferences as diverse as MobiCom, CHI, PADS, and AAMAS. This means that there is a big and diverse community of computer scientists and computer engineers who tackle research that is connected to the development of intelligent traffic and transportation systems. It is also possible to see that this community is growing, and that research projects are getting more and more interdisciplinary. To foster the cooperation among the involved communities, this book aims at giving a broad introduction into the basic but relevant concepts related to transportation systems, targeting researchers and practitioners from computer science and information technology. In addition, the second part of the book gives a panorama of some of the most exciting and newest technologies, originating in computer science and computer engineering, that are now being employed in projects related to car-to-car communication, interconnected vehicles, car navigation, platooning, crowd sensing and sensor networks, among others. This material will also be of interest to engineers and researchers from the traffic and transportation community.Table of ContentsPreface.- Acknowledgments.- List of Symbols.- Introduction.- Elements of Supply.- Elements of Demand.- Traffic Assignment: Connecting Supply and Demand.- Getting Data for Demand Estimation and Traffic Flow Modeling.- Modeling and Simulation of Advanced Decision Making.- Intelligent Measures in Control and Management.- Driver Support and Guidance.- Trends and New Technologies.- Bibliography.- Authors' Biographies.
£25.19
Springer International Publishing AG An Introduction to Constraint-Based Temporal Reasoning
Book SynopsisSolving challenging computational problems involving time has been a critical component in the development of artificial intelligence systems almost since the inception of the field. This book provides a concise introduction to the core computational elements of temporal reasoning for use in AI systems for planning and scheduling, as well as systems that extract temporal information from data. It presents a survey of temporal frameworks based on constraints, both qualitative and quantitative, as well as of major temporal consistency techniques. The book also introduces the reader to more recent extensions to the core model that allow AI systems to explicitly represent temporal preferences and temporal uncertainty. This book is intended for students and researchers interested in constraint-based temporal reasoning. It provides a self-contained guide to the different representations of time, as well as examples of recent applications of time in AI systems.Table of ContentsPreface.- Summary of Acronyms.- Introduction to Time in AI Systems.- Temporal Frameworks Based on Constraints.- Extensions: Preferences and Uncertainty.- Applications of Temporal Reasoning.- Bibliography.- Authors' Biographies .
£25.19
Springer International Publishing AG Judgment Aggregation: A Primer
Book SynopsisJudgment aggregation is a mathematical theory of collective decision-making. It concerns the methods whereby individual opinions about logically interconnected issues of interest can, or cannot, be aggregated into one collective stance. Aggregation problems have traditionally been of interest for disciplines like economics and the political sciences, as well as philosophy, where judgment aggregation itself originates from, but have recently captured the attention of disciplines like computer science, artificial intelligence and multi-agent systems. Judgment aggregation has emerged in the last decade as a unifying paradigm for the formalization and understanding of aggregation problems. Still, no comprehensive presentation of the theory is available to date. This Synthesis Lecture aims at filling this gap presenting the key motivations, results, abstractions and techniques underpinning it. Table of Contents: Preface / Acknowledgments / Logic Meets Social Choice Theory / Basic Concepts / Impossibility / Coping with Impossibility / Manipulability / Aggregation Rules / Deliberation / Bibliography / Authors' Biographies / IndexTable of ContentsPreface.- Acknowledgments.- Logic Meets Social Choice Theory.- Basic Concepts.- Impossibility.- Coping with Impossibility.- Manipulability.- Aggregation Rules.- Deliberation.- Bibliography.- Authors' Biographies.- Index .
£26.99
Springer International Publishing AG General Game Playing
Book SynopsisGeneral game players are computer systems able to play strategy games based solely on formal game descriptions supplied at "runtime" (n other words, they don't know the rules until the game starts). Unlike specialized game players, such as Deep Blue, general game players cannot rely on algorithms designed in advance for specific games; they must discover such algorithms themselves. General game playing expertise depends on intelligence on the part of the game player and not just intelligence of the programmer of the game player. GGP is an interesting application in its own right. It is intellectually engaging and more than a little fun. But it is much more than that. It provides a theoretical framework for modeling discrete dynamic systems and defining rationality in a way that takes into account problem representation and complexities like incompleteness of information and resource bounds. It has practical applications in areas where these features are important, e.g., in business and law. More fundamentally, it raises questions about the nature of intelligence and serves as a laboratory in which to evaluate competing approaches to artificial intelligence. This book is an elementary introduction to General Game Playing (GGP). (1) It presents the theory of General Game Playing and leading GGP technologies. (2) It shows how to create GGP programs capable of competing against other programs and humans. (3) It offers a glimpse of some of the real-world applications of General Game Playing.Table of ContentsPreface.- Introduction.- Game Description.- Game Management.- Game Playing.- Small Single-Player Games.- Small Multiple-Player Games.- Heuristic Search.- Probabilistic Search.- Propositional Nets.- General Game Playing With Propnets.- Factoring.- Discovery of Heuristics.- Logic.- Analyzing Games with Logic.- Solving Single-Player Games with Logic.- Discovering Heuristics with Logic.- Games with Incomplete Information.- Games with Historical Constraints.- Incomplete Game Descriptions.- Advanced General Game Playing.- Authors' Biographies.
£26.99
Springer International Publishing AG Robot Learning from Human Teachers
Book SynopsisLearning from Demonstration (LfD) explores techniques for learning a task policy from examples provided by a human teacher. The field of LfD has grown into an extensive body of literature over the past 30 years, with a wide variety of approaches for encoding human demonstrations and modeling skills and tasks. Additionally, we have recently seen a focus on gathering data from non-expert human teachers (i.e., domain experts but not robotics experts). In this book, we provide an introduction to the field with a focus on the unique technical challenges associated with designing robots that learn from naive human teachers. We begin, in the introduction, with a unification of the various terminology seen in the literature as well as an outline of the design choices one has in designing an LfD system. Chapter 2 gives a brief survey of the psychology literature that provides insights from human social learning that are relevant to designing robotic social learners. Chapter 3 walks through an LfD interaction, surveying the design choices one makes and state of the art approaches in prior work. First, is the choice of input, how the human teacher interacts with the robot to provide demonstrations. Next, is the choice of modeling technique. Currently, there is a dichotomy in the field between approaches that model low-level motor skills and those that model high-level tasks composed of primitive actions. We devote a chapter to each of these. Chapter 7 is devoted to interactive and active learning approaches that allow the robot to refine an existing task model. And finally, Chapter 8 provides best practices for evaluation of LfD systems, with a focus on how to approach experiments with human subjects in this domain.Table of ContentsIntroduction.- Human Social Learning.- Modes of Interaction with a Teacher.- Learning Low-Level Motion Trajectories.- Learning High-Level Tasks.- Refining a Learned Task.- Designing and Evaluating an LfD Study.- Future Challenges and Opportunities.- Bibliography.- Authors' Biographies.
£26.59
Springer International Publishing AG Graph-Based Semi-Supervised Learning
Book SynopsisWhile labeled data is expensive to prepare, ever increasing amounts of unlabeled data is becoming widely available. In order to adapt to this phenomenon, several semi-supervised learning (SSL) algorithms, which learn from labeled as well as unlabeled data, have been developed. In a separate line of work, researchers have started to realize that graphs provide a natural way to represent data in a variety of domains. Graph-based SSL algorithms, which bring together these two lines of work, have been shown to outperform the state-of-the-art in many applications in speech processing, computer vision, natural language processing, and other areas of Artificial Intelligence. Recognizing this promising and emerging area of research, this synthesis lecture focuses on graph-based SSL algorithms (e.g., label propagation methods). Our hope is that after reading this book, the reader will walk away with the following: (1) an in-depth knowledge of the current state-of-the-art in graph-based SSL algorithms, and the ability to implement them; (2) the ability to decide on the suitability of graph-based SSL methods for a problem; and (3) familiarity with different applications where graph-based SSL methods have been successfully applied. Table of Contents: Introduction / Graph Construction / Learning and Inference / Scalability / Applications / Future Work / Bibliography / Authors' Biographies / IndexTable of ContentsIntroduction.- Graph Construction.- Learning and Inference.- Scalability.- Applications.- Future Work.- Bibliography.- Authors' Biographies.- Index .
£25.19
Springer International Publishing AG Metric Learning
Book SynopsisSimilarity between objects plays an important role in both human cognitive processes and artificial systems for recognition and categorization. How to appropriately measure such similarities for a given task is crucial to the performance of many machine learning, pattern recognition and data mining methods. This book is devoted to metric learning, a set of techniques to automatically learn similarity and distance functions from data that has attracted a lot of interest in machine learning and related fields in the past ten years. In this book, we provide a thorough review of the metric learning literature that covers algorithms, theory and applications for both numerical and structured data. We first introduce relevant definitions and classic metric functions, as well as examples of their use in machine learning and data mining. We then review a wide range of metric learning algorithms, starting with the simple setting of linear distance and similarity learning. We show how one may scale-up these methods to very large amounts of training data. To go beyond the linear case, we discuss methods that learn nonlinear metrics or multiple linear metrics throughout the feature space, and review methods for more complex settings such as multi-task and semi-supervised learning. Although most of the existing work has focused on numerical data, we cover the literature on metric learning for structured data like strings, trees, graphs and time series. In the more technical part of the book, we present some recent statistical frameworks for analyzing the generalization performance in metric learning and derive results for some of the algorithms presented earlier. Finally, we illustrate the relevance of metric learning in real-world problems through a series of successful applications to computer vision, bioinformatics and information retrieval. Table of Contents: Introduction / Metrics / Properties of Metric Learning Algorithms / Linear Metric Learning / Nonlinear and Local Metric Learning / Metric Learning for Special Settings / Metric Learning for Structured Data / Generalization Guarantees for Metric Learning / Applications / Conclusion / Bibliography / Authors' BiographiesTable of ContentsIntroduction.- Metrics.- Properties of Metric Learning Algorithms.- Linear Metric Learning.- Nonlinear and Local Metric Learning.- Metric Learning for Special Settings.- Metric Learning for Structured Data.- Generalization Guarantees for Metric Learning.- Applications.- Conclusion.- Bibliography.- Authors' Biographies .
£42.74
Springer International Publishing AG Representing and Reasoning with Qualitative Preferences: Tools and Applications
Book SynopsisThis book provides a tutorial introduction to modern techniques for representing and reasoning about qualitative preferences with respect to a set of alternatives. The syntax and semantics of several languages for representing preference languages, including CP-nets, TCP-nets, CI-nets, and CP-theories, are reviewed. Some key problems in reasoning about preferences are introduced, including determining whether one alternative is preferred to another, or whether they are equivalent, with respect to a given set of preferences. These tasks can be reduced to model checking in temporal logic. Specifically, an induced preference graph that represents a given set of preferences can be efficiently encoded using a Kripke Structure for Computational Tree Logic (CTL). One can translate preference queries with respect to a set of preferences into an equivalent set of formulae in CTL, such that the CTL formula is satisfied whenever the preference query holds. This allows us to use a model checker to reason about preferences, i.e., answer preference queries, and to obtain a justification as to why a preference query is satisfied (or not) with respect to a set of preferences. This book defines the notions of the equivalence of two sets of preferences, including what it means for one set of preferences to subsume another, and shows how to answer preferential equivalence and subsumption queries using model checking. Furthermore, this book demontrates how to generate alternatives ordered by preference, along with providing ways to deal with inconsistent preference specifications. A description of CRISNER—an open source software implementation of the model checking approach to qualitative preference reasoning in CP-nets, TCP-nets, and CP-theories is included, as well as examples illustrating its use.Table of ContentsAcknowledgments.- Qualitative Preferences.- Qualitative Preference Languages.- Model Checking and Computation Tree Logic.- Dominance Testing via Model Checking.- Verifying Preference Equivalence and Subsumption.- Ordering Alternatives With Respect to Preference.- CRISNER: A Practically Efficient Reasoner for Qualitative Preferences.- Postscript.- Bibliography.- Authors' Biographies .
£31.49
Springer International Publishing AG Multi-Objective Decision Making
Book SynopsisMany real-world decision problems have multiple objectives. For example, when choosing a medical treatment plan, we want to maximize the efficacy of the treatment, but also minimize the side effects. These objectives typically conflict, e.g., we can often increase the efficacy of the treatment, but at the cost of more severe side effects. In this book, we outline how to deal with multiple objectives in decision-theoretic planning and reinforcement learning algorithms. To illustrate this, we employ the popular problem classes of multi-objective Markov decision processes (MOMDPs) and multi-objective coordination graphs (MO-CoGs). First, we discuss different use cases for multi-objective decision making, and why they often necessitate explicitly multi-objective algorithms. We advocate a utility-based approach to multi-objective decision making, i.e., that what constitutes an optimal solution to a multi-objective decision problem should be derived from the available information about user utility. We show how different assumptions about user utility and what types of policies are allowed lead to different solution concepts, which we outline in a taxonomy of multi-objective decision problems. Second, we show how to create new methods for multi-objective decision making using existing single-objective methods as a basis. Focusing on planning, we describe two ways to creating multi-objective algorithms: in the inner loop approach, the inner workings of a single-objective method are adapted to work with multi-objective solution concepts; in the outer loop approach, a wrapper is created around a single-objective method that solves the multi-objective problem as a series of single-objective problems. After discussing the creation of such methods for the planning setting, we discuss how these approaches apply to the learning setting. Next, we discuss three promising application domains for multi-objective decision making algorithms: energy, health, and infrastructure and transportation. Finally, we conclude by outlining important open problems and promising future directions. Table of ContentsPreface.- Acknowledgments.- Table of Abbreviations.- Introduction.- Multi-Objective Decision Problems.- Taxonomy.- Inner Loop Planning.- Outer Loop Planning.- Learning.- Applications.- Conclusions and Future Work.- Bibliography.- Authors' Biographies .
£25.19
Springer International Publishing AG Adversarial Machine Learning
Book SynopsisThe increasing abundance of large high-quality datasets, combined with significant technical advances over the last several decades have made machine learning into a major tool employed across a broad array of tasks including vision, language, finance, and security. However, success has been accompanied with important new challenges: many applications of machine learning are adversarial in nature. Some are adversarial because they are safety critical, such as autonomous driving. An adversary in these applications can be a malicious party aimed at causing congestion or accidents, or may even model unusual situations that expose vulnerabilities in the prediction engine. Other applications are adversarial because their task and/or the data they use are. For example, an important class of problems in security involves detection, such as malware, spam, and intrusion detection. The use of machine learning for detecting malicious entities creates an incentive among adversaries to evade detection by changing their behavior or the content of malicius objects they develop. The field of adversarial machine learning has emerged to study vulnerabilities of machine learning approaches in adversarial settings and to develop techniques to make learning robust to adversarial manipulation. This book provides a technical overview of this field. After reviewing machine learning concepts and approaches, as well as common use cases of these in adversarial settings, we present a general categorization of attacks on machine learning. We then address two major categories of attacks and associated defenses: decision-time attacks, in which an adversary changes the nature of instances seen by a learned model at the time of prediction in order to cause errors, and poisoning or training time attacks, in which the actual training dataset is maliciously modified. In our final chapter devoted to technical content, we discuss recent techniques for attacks on deep learning, as well as approaches for improving robustness of deep neural networks. We conclude with a discussion of several important issues in the area of adversarial learning that in our view warrant further research. Given the increasing interest in the area of adversarial machine learning, we hope this book provides readers with the tools necessary to successfully engage in research and practice of machine learning in adversarial settings.Table of ContentsList of Figures.- Preface.- Acknowledgments.- Introduction.- Machine Learning Preliminaries.- Categories of Attacks on Machine Learning.- Attacks at Decision Time.- Defending Against Decision-Time Attacks.- Data Poisoning Attacks.- Defending Against Data Poisoning.- Attacking and Defending Deep Learning.- The Road Ahead.- Bibliography.- Authors' Biographies.- Index .
£47.49
Springer International Publishing AG Lifelong Machine Learning, Second Edition
Book SynopsisLifelong Machine Learning, Second Edition is an introduction to an advanced machine learning paradigm that continuously learns by accumulating past knowledge that it then uses in future learning and problem solving. In contrast, the current dominant machine learning paradigm learns in isolation: given a training dataset, it runs a machine learning algorithm on the dataset to produce a model that is then used in its intended application. It makes no attempt to retain the learned knowledge and use it in subsequent learning. Unlike this isolated system, humans learn effectively with only a few examples precisely because our learning is very knowledge-driven: the knowledge learned in the past helps us learn new things with little data or effort. Lifelong learning aims to emulate this capability, because without it, an AI system cannot be considered truly intelligent. Research in lifelong learning has developed significantly in the relatively short time since the first edition of this book was published. The purpose of this second edition is to expand the definition of lifelong learning, update the content of several chapters, and add a new chapter about continual learning in deep neural networks—which has been actively researched over the past two or three years. A few chapters have also been reorganized to make each of them more coherent for the reader. Moreover, the authors want to propose a unified framework for the research area. Currently, there are several research topics in machine learning that are closely related to lifelong learning—most notably, multi-task learning, transfer learning, and meta-learning—because they also employ the idea of knowledge sharing and transfer. This book brings all these topics under one roof and discusses their similarities and differences. Its goal is to introduce this emerging machine learning paradigm and present a comprehensive survey and review of the important research results and latest ideas in the area. This book is thus suitable for students, researchers, and practitioners who are interested in machine learning, data mining, natural language processing, or pattern recognition. Lecturers can readily use the book for courses in any of these related fields.Table of ContentsPreface.- Acknowledgments.- Introduction.- Related Learning Paradigms.- Lifelong Supervised Learning.- Continual Learning and Catastrophic Forgetting.- Open-World Learning.- Lifelong Topic Modeling.- Lifelong Information Extraction.- Continuous Knowledge Learning in Chatbots.- Lifelong Reinforcement Learning.- Conclusion and Future Directions.- Bibliography.- Authors' Biographies.
£49.49
Springer International Publishing AG Federated Learning
Book SynopsisHow is it possible to allow multiple data owners to collaboratively train and use a shared prediction model while keeping all the local training data private? Traditional machine learning approaches need to combine all data at one location, typically a data center, which may very well violate the laws on user privacy and data confidentiality. Today, many parts of the world demand that technology companies treat user data carefully according to user-privacy laws. The European Union's General Data Protection Regulation (GDPR) is a prime example. In this book, we describe how federated machine learning addresses this problem with novel solutions combining distributed machine learning, cryptography and security, and incentive mechanism design based on economic principles and game theory. We explain different types of privacy-preserving machine learning solutions and their technological backgrounds, and highlight some representative practical use cases. We show how federated learning can become the foundation of next-generation machine learning that caters to technological and societal needs for responsible AI development and application.Table of ContentsPreface.- Acknowledgments.- Introduction.- Background.- Distributed Machine Learning.- Horizontal Federated Learning.- Vertical Federated Learning.- Federated Transfer Learning.- Incentive Mechanism Design for Federated Learning.- Federated Learning for Vision, Language, and Recommendation.- Federated Reinforcement Learning.- Selected Applications.- Summary and Outlook.- Bibliography.- Authors' Biographies.
£49.49
Springer International Publishing AG Introduction to Symbolic Plan and Goal Recognition
Book SynopsisPlan recognition, activity recognition, and goal recognition all involve making inferences about other actors based on observations of their interactions with the environment and other agents. This synergistic area of research combines, unites, and makes use of techniques and research from a wide range of areas including user modeling, machine vision, automated planning, intelligent user interfaces, human-computer interaction, autonomous and multi-agent systems, natural language understanding, and machine learning. It plays a crucial role in a wide variety of applications including assistive technology, software assistants, computer and network security, human-robot collaboration, natural language processing, video games, and many more. This wide range of applications and disciplines has produced a wealth of ideas, models, tools, and results in the recognition literature. However, it has also contributed to fragmentation in the field, with researchers publishing relevant results in a wide spectrum of journals and conferences. This book seeks to address this fragmentation by providing a high-level introduction and historical overview of the plan and goal recognition literature. It provides a description of the core elements that comprise these recognition problems and practical advice for modeling them. In particular, we define and distinguish the different recognition tasks. We formalize the major approaches to modeling these problems using a single motivating example. Finally, we describe a number of state-of-the-art systems and their extensions, future challenges, and some potential applications.Table of ContentsPreface.- Acknowledgments.- Introduction.- Defining a Recognition Problem.- Implicit vs. Explicit Representation of Knowledge.- Improving a Recognizer.- Future Directions.- Bibliography.- Authors' Biographies.
£40.49
Springer International Publishing AG Thinking Data Science: A Data Science
Book SynopsisThis definitive guide to Machine Learning projects answers the problems an aspiring or experienced data scientist frequently has: Confused on what technology to use for your ML development? Should I use GOFAI, ANN/DNN or Transfer Learning? Can I rely on AutoML for model development? What if the client provides me Gig and Terabytes of data for developing analytic models? How do I handle high-frequency dynamic datasets? This book provides the practitioner with a consolidation of the entire data science process in a single “Cheat Sheet”.The challenge for a data scientist is to extract meaningful information from huge datasets that will help to create better strategies for businesses. Many Machine Learning algorithms and Neural Networks are designed to do analytics on such datasets. For a data scientist, it is a daunting decision as to which algorithm to use for a given dataset. Although there is no single answer to this question, a systematic approach to problem solving is necessary. This book describes the various ML algorithms conceptually and defines/discusses a process in the selection of ML/DL models. The consolidation of available algorithms and techniques for designing efficient ML models is the key aspect of this book. Thinking Data Science will help practising data scientists, academicians, researchers, and students who want to build ML models using the appropriate algorithms and architectures, whether the data be small or big. Table of Contents1. Data Science Process2. Dimensionality Reduction - Creating Manageable Training Datasets3. Classical Algorithms - Overview4. Regression Analysis5. Decision Tree6. Ensemble - Bagging and Boosting7. K-Nearest Neighbors8. Naive Bayes9. Support Vector Machines: A supervised learning algorithm for Classification and Regression10. Clustering Overview11. Centroid-based Clustering12. Connectivity-based Clustering13. Gaussian Mixture Model14. Density-based15. BIRCH16. CLARANS17. Affinity Propagation Clustering18. STING19. CLIQUE20. Artificial Neural Networks21. ANN-based Applications22. Automated Tools23. Data Scientist’s Ultimate Workflow
£49.49
Springer International Publishing AG xxAI - Beyond Explainable AI: International Workshop, Held in Conjunction with ICML 2020, July 18, 2020, Vienna, Austria, Revised and Extended Papers
Book SynopsisThis is an open access book.Statistical machine learning (ML) has triggered a renaissance of artificial intelligence (AI). While the most successful ML models, including Deep Neural Networks (DNN), have developed better predictivity, they have become increasingly complex, at the expense of human interpretability (correlation vs. causality). The field of explainable AI (xAI) has emerged with the goal of creating tools and models that are both predictive and interpretable and understandable for humans. Explainable AI is receiving huge interest in the machine learning and AI research communities, across academia, industry, and government, and there is now an excellent opportunity to push towards successful explainable AI applications. This volume will help the research community to accelerate this process, to promote a more systematic use of explainable AI to improve models in diverse applications, and ultimately to better understand how current explainable AI methods need to be improved and what kind of theory of explainable AI is needed. After overviews of current methods and challenges, the editors include chapters that describe new developments in explainable AI. The contributions are from leading researchers in the field, drawn from both academia and industry, and many of the chapters take a clear interdisciplinary approach to problem-solving. The concepts discussed include explainability, causability, and AI interfaces with humans, and the applications include image processing, natural language, law, fairness, and climate science.Table of ContentsEditorial.- xxAI - Beyond explainable Artificial Intelligence.- Current Methods and Challenges.- Explainable AI Methods - A Brief Overview.- Challenges in Deploying Explainable Machine Learning.- Methods for Machine Learning Models.- CLEVR-X: A Visual Reasoning Dataset for Natural Language Explanations.- New Developments in Explainable AI.- A Rate-Distortion Framework for Explaining Black-box Model Decisions.- Explaining the Predictions of Unsupervised Learning Models.- Towards Causal Algorithmic Recourse.- Interpreting Generative Adversarial Networks for Interactive Image Generation.- XAI and Strategy Extraction via Reward Redistribution.- Interpretable, Verifiable, and Robust Reinforcement Learning via Program Synthesis.- Interpreting and improving deep-learning models with reality checks.- Beyond the Visual Analysis of Deep Model Saliency.- ECQ^2: Quantization for Low-Bit and Sparse DNNs.- A whale’s tail - Finding the right whale in an uncertain world.- Explainable Artificial Intelligence in Meteorology and Climate Science: Model fine-tuning, calibrating trust and learning new science.- An Interdisciplinary Approach to Explainable AI.-Varieties of AI Explanations under the Law - From the GDPR to the AIA, and beyond.- Towards Explainability for AI Fairness.- Logic and Pragmatics in AI Explanation.
£31.49
Springer International Publishing AG Dimensionality Reduction in Data Science
Book SynopsisThis book provides a practical and fairly comprehensive review of Data Science through the lens of dimensionality reduction, as well as hands-on techniques to tackle problems with data collected in the real world. State-of-the-art results and solutions from statistics, computer science and mathematics are explained from the point of view of a practitioner in any domain science, such as biology, cyber security, chemistry, sports science and many others. Quantitative and qualitative assessment methods are described to implement and validate the solutions back in the real world where the problems originated.The ability to generate, gather and store volumes of data in the order of tera- and exo bytes daily has far outpaced our ability to derive useful information with available computational resources for many domains.This book focuses on data science and problem definition, data cleansing, feature selection and extraction, statistical, geometric, information-theoretic, biomolecular and machine learning methods for dimensionality reduction of big datasets and problem solving, as well as a comparative assessment of solutions in a real-world setting.This book targets professionals working within related fields with an undergraduate degree in any science area, particularly quantitative. Readers should be able to follow examples in this book that introduce each method or technique. These motivating examples are followed by precise definitions of the technical concepts required and presentation of the results in general situations. These concepts require a degree of abstraction that can be followed by re-interpreting concepts like in the original example(s). Finally, each section closes with solutions to the original problem(s) afforded by these techniques, perhaps in various ways to compare and contrast dis/advantages to other solutions.Table of Contents1. What is Data Science (DS)?1.1 Major Families of Data Science Problems1.1.1 Classification Problems1.1.2 Prediction Problems1.1.3 Clustering Problems1.2 Data, Big Data and Pre-processing1.2.1 What is Data?1.2.2 Big data1.2.3 Data Cleansing1.2.4 Data Visualization1.2.5 Data Understanding1.3 Populations and Data Sampling1.3.1 Sampling1.3.2 Training, Testing and Validation1.4 Overview and Scope1.4.1 Prerequisites and Layout1.4.2 Data Science Methodology1.4.3 Scope of the Book2. Solutions to Data Science Problems2.1 Conventional Statistical Solutions2.1.1 Linear Multiple Regression Model: Continuous Response2.1.2 Logistic Regression: Categorical Response2.1.3 Variable Selection and Model Building2.1.4 Generalized Linear Model (GLM)2.1.5 Decision Trees2.1.6 Bayesian Learning2.2 Machine Learning Solutions: Supervised2.2.1 k-Nearest Neighbors (kNN)2.2.2 Ensemble Methods2.2.3 Support Vector Machines (SVMs)2.2.4 Neural Networks (NNs)2.3 Machine Learning Solutions: Unsupervised2.3.1 Hard Clustering2.3.2 Soft Clustering2.4 Controls, Evaluation and Assessment2.4.1 Evaluation Methods2.4.2 Metrics for Assessment3. What is Dimensionality Reduction (DR)?3.1 Dimensionality Reduction3.2 Major Approaches to Dimensionality Reduction3.2.1 Conventional Statistical Approaches3.2.2 Geometric Approaches3.2.3 Information-theoretic Approaches3.2.4 Molecular Computing Approaches3.3 The Blessings of Dimensionality4. Conventional Statistical Approaches4.1 Principal Component Analysis (PCA)4.1.1 Obtaining the Principal Components4.1.2 Singular value decomposition (SVD)4.2 Nonlinear PCA 4.2.1 Kernel PCA4.2.2 Independent component analysis (ICA)4.3 Nonnegative Matrix Factorization (NMF)4.3.1 Approximate Solutions4.3.2 Clustering and Other Applications4.4 Discriminant Analysis4.4.1 Linear discriminant analysis (LDA)4.4.2 Quadratic discriminant analysis (QDA)4.5 Sliced Inverse Regression (SIR)5. Geometric Approaches5.1 Introduction to Manifolds5.2 Manifold Learning Methods5.2.1 Multi-Dimensional Scaling (MDS)5.2.2 Isometric Mapping (ISOMAP)5.2.3 t-Stochastic Neighbor Embedding ( t-SNE )5.3 Exploiting Randomness (RND)6. Information-theoretic Approaches6.1 Shannon Entropy (H)6.2 Reduction by Conditional Entropy6.3 Reduction by Iterated Conditional Entropy6.4 Reduction by Conditional Entropy on Targets6.5 Other Variations7. Molecular Computing Approaches7.1 Encoding Abiotic Data into DNA7.2 Deep Structure of DNA Spaces7.2.1 Structural Properties of DNA Spaces7.2.2 Noncrosshybridizing (nxh) Bases7.3 Reduction by Genomic Signatures7.3.1 Background7.3.2 Genomic Signatures7.4 Reduction by Pmeric Signatures8. Statistical Learning Approaches8.1 Reduction by Multiple Regression8.2 Reduction by Ridge Regression8.3 Reduction by Lasso Regression 8.4 Selection versus Shrinkage8.5 Further refinements9. Machine Learning Approaches9.1 Autoassociative Feature Encoders9.1.1 Undercomplete Autoencoders 9.1.2 Sparse Autoencoders9.1.3 Variational Autoencoders9.1.4 Dimensionality Reduction in MNIST Images9.2 Neural Feature Selection9.2.1 Facial Features, Expressions and Displays9.2.2 The Cohn-Kanade Dataset9.2.3 Primary and Derived Features9.3 Other Methods10. Metaheuristics of DR Methods10.1 Exploiting Feature Grouping10.2 Exploiting Domain Knowledge10.2.1 What is Domain Knowledge?10.2.2 Domain Knowledge for Dimensionality Reduction10.3 Heuristic Rules for Feature Selection, Extraction and Number10.4 About Explainability of Solutions10.4.1 What is Explainability?10.4.2 Explainability in Dimensionality Reduction10.5 Choosing Wisely10.6 About the Curse of Dimensionality10.7 About the No-Free-Lunch Theorem (NFL)11. Appendices11.1 Statistics and Probability Background11.1.1 Commonly Used Discrete Distributions11.1.2 Commonly Used Continuous Distributions11.1.3 Major Results In Probability and Statistics11.2 Linear Algebra Background11.2.1 Fields, Vector Spaces and Subspaces11.2.2 Linear independence, Bases and Dimension11.2.3 Linear Transformations and Matrices11.2.4 Eigenvalues and Spectral Decomposition11.3 Computer Science Background11.3.1 Computational Science and Complexity11.3.2 Machine Learning11.4 Typical Data Science Problems11.5 A Sample of Common and Big Datasets11.6 Computing Platforms11.6.1 The Environment R11.6.2 Python environmentsReferences
£49.49
Springer International Publishing AG Image Analysis and Processing – ICIAP 2022: 21st International Conference, Lecce, Italy, May 23–27, 2022, Proceedings, Part III
Book SynopsisThe proceedings set LNCS 13231, 13232, and 13233 constitutes the refereed proceedings of the 21st International Conference on Image Analysis and Processing, ICIAP 2022, which was held during May 23-27, 2022, in Lecce, Italy,The 168 papers included in the proceedings were carefully reviewed and selected from 307 submissions. They deal with video analysis and understanding; pattern recognition and machine learning; deep learning; multi-view geometry and 3D computer vision; image analysis, detection and recognition; multimedia; biomedical and assistive technology; digital forensics and biometrics; image processing for cultural heritage; robot vision; etc. Table of ContentsPattern Recognition and Machine Learning.- Video Analysis & Understanding.- Special Session.
£62.99
Springer International Publishing AG Classification and Data Science in the Digital Age
Book SynopsisThe contributions gathered in this open access book focus on modern methods for data science and classification and present a series of real-world applications. Numerous research topics are covered, ranging from statistical inference and modeling to clustering and dimension reduction, from functional data analysis to time series analysis, and network analysis. The applications reflect new analyses in a variety of fields, including medicine, marketing, genetics, engineering, and education.The book comprises selected and peer-reviewed papers presented at the 17th Conference of the International Federation of Classification Societies (IFCS 2022), held in Porto, Portugal, July 19–23, 2022. The IFCS federates the classification societies and the IFCS biennial conference brings together researchers and stakeholders in the areas of Data Science, Classification, and Machine Learning. It provides a forum for presenting high-quality theoretical and applied works, and promoting and fostering interdisciplinary research and international cooperation. The intended audience is researchers and practitioners who seek the latest developments and applications in the field of data science and classification.Table of ContentsPreface.- R. Abdesselam: A Topological Clustering of Individuals.- C. Anton and I. Smith: Model Based Clustering of Functional Data with Mild Outliers.- F. Antonazzo and S. Ingrassia: A Trivariate Geometric Classification of Decision Boundaries for Mixtures of Regressions.- E. Arnone, E. Cunial, and L. M. Sangalli: Generalized Spatio-temporal Regression with PDE Penalization.- R. Ascari and S. Migliorati: A New Regression Model for the Analysis of Microbiome Data.- R. Aschenbruck, G. Szepannek, and A. F. X. Wilhelm: Stability of Mixed-type Cluster Partitions for Determination of the Number of Clusters.- A. Ashofteh and P. Campos: A Review on Official Survey Item Classification for Mixed-Mode Effects Adjustment.- V. Batagelj: Clustering and Blockmodeling Temporal Networks – Two Indirect Approaches.- R. Boutalbi, L. Labiod, and M. Nadif: Latent Block Regression Model.- N. Chabane, M. Achraf Bouaoune, R. Amir Sofiane Tighilt, B. Mazoure, N. Tahiri, and V. Makarenkov: Using Clustering and Machine Learning Methods to Provide Intelligent Grocery Shopping Recommendations.- T. Chadjipadelis and S. Magopoulou: COVID-19 Pandemic: a Methodological Model for the Analysis of Government’s Preventing Measures and Health Data Records.- J. Champagne Gareau, É. Beaudry, and V. Makarenkov: pcTVI: Parallel MDP Solver Using a Decomposition into Independent Chains.- C. Di Nuzzo and S. Ingrassia: Three-way Spectral Clustering.- J. Dobša and H. A. L. Kiers: Improving Classification of Documents by Semi-supervised Clustering in a Semantic Space.- J. Gama: Trends in Data Stream Mining.- L. A. García-Escudero, A. Mayo-Iscar, G. Morelli, and M. Riani: Old and New Constraints in Model Based Clustering.- V. G Genova, G. Giordano, G . Ragozini, and M. Prosperina Vitale: Clustering Student Mobility Data in 3-way Networks.- R. Giubilei: Clustering Brain Connectomes Through a Density-peak Approach.- T. Górecki, M. Šuczak, and P. Piasecki: Similarity Forest for Time Series Classification.- K. Hayashi, E. Hoshino, M. Suzuki, E. Nakanishi, K. Sakai, and M. Obatake: Detection of the Biliary Atresia Using Deep Convolutional Neural Networks Based on Statistical Learning Weights via Optimal Similarity and Resampling Methods.- Ch. Hennig: Some Issues in Robust Clustering.- J. Kalina and P. Janá£ek: Robustness Aspects of Optimized Centroids.- L. Labiod and M. Nadif: Data Clustering and Representation Learning Based on Networked Data.- Lazhar Labiod and Mohamed Nadif: Towards a Bi-stochastic Matrix Approximation of k-means and Some Variants.- A. LaLonde, T. Love, D. R. Young, and T. Wu: Clustering Adolescent Female Physical Activity Levels with an Infinite Mixture Model on Random Effects.- Á. López-Oriona, J. A. Vilar, and P. D’Urso: Unsupervised Classification of Categorical Time Series Through Innovative Distances.- D. Masís, E. Segura, J. Trejos, and A. Xavier: Fuzzy Clustering by Hyperbolic Smoothing.- R. Meng, H. K. H. Lee, and K. Bouchard: Stochastic Collapsed Variational Inference for Structured Gaussian Process Regression Networks.- H. Duy Nguyen, F. Forbes, G. Fort, and O. Cappé: An Online Minorization-Maximization Algorithm.- L. Palazzo and R. Ievoli: Detecting Differences in Italian Regional Health Services During Two Covid-19 Waves.- G. Panagiotidou and T. Chadjipadelis: Political and Religion Attitudes in Greece: Behavioral Discourses.- K. Pawlasová, I. Karafiátová, and J. Dvořák: Supervised Classification via Neural Networks for Replicated Point Patterns.- G. Perrone and G. Soffritti: Parsimonious Mixtures of Seemingly Unrelated Contaminated Normal Regression Models.- N. Pronello, R. Ignaccolo, L. Ippoliti, and S. Fontanella: Penalized Model-based Functional Clustering: a Regularization Approach via Shrinkage Methods.- D. Rodrigues, L. P. Reis, and B. M. Faria: Emotion Classification Based on Single Electrode Brain Data: Applications for Assistive Technology.- R. Scimone, A. Menafoglio, L. M. Sangalli, and P. Secchi: The Death Process in Italy Before and During the Covid-19 Pandemic: a Functional Compositional Approach.- O. Silva, Á. Sousa, and H. Bacelar-Nicolau: Clustering Validation in the Context of Hierarchical Cluster Analysis: an Empirical Study.- C. Silvestre, M. G. M. S. Cardoso, and M. Figueiredo: An MML Embedded Approach for Estimating the Number of Clusters.- Á. Sousa, O. Silva, M. Graça Batista, S. Cabral, and H. Bacelar-Nicolau: Typology of Motivation Factors for Employees in the Banking Sector: An Empirical Study Using Multivariate Data Analysis Methods.- J. Michael Spoor, J. Weber, and J. Ovtcharova: A Proposal for Formalization and Definition of Anomalies in Dynamical Systems.- N. Tahiri and A. Koshkarov: New Metrics for Classifying Phylogenetic Trees Using -means and the Symmetric Difference Metric.- S. D. Tomarchio: On Parsimonious Modelling via Matrix-variate t Mixtures.- G. Zammarchi, M. Romano, and C. Conversano: Evolution of Media Coverage on Climate Change and Environmental Awareness: an Analysis of Tweets from UK and US Newspapers.
£31.49
Springer International Publishing AG Smart Applications with Advanced Machine Learning
Book SynopsisThis book brings together the most recent, quality research papers accepted and presented in the 3rd International Conference on Artificial Intelligence and Applied Mathematics in Engineering (ICAIAME 2021) held in Antalya, Turkey between 1-3 October 2021. Objective of the content is to provide important and innovative research for developments-improvements within different engineering fields, which are highly interested in using artificial intelligence and applied mathematics. As a collection of the outputs from the ICAIAME 2021, the book is specifically considering research outcomes including advanced use of machine learning and careful problem designs on human-centred aspects. In this context, it aims to provide recent applications for real-world improvements making life easier and more sustainable for especially humans. The book targets the researchers, degree students, and practitioners from both academia and the industry.
£151.99
Springer International Publishing AG Biomedical Image Registration: 10th International Workshop, WBIR 2022, Munich, Germany, July 10–12, 2022, Proceedings
Book SynopsisThis book constitutes the refereed proceedings of the 10th International Workshop on Biomedical Image Registration, WBIR 2020, which was supposed to be held in Munich, Germany, in July 2022.The 11 full and poster papers together with 17 short papers included in this volume were carefully reviewed and selected from 32 submitted papers. The papers are organized in the following topical sections: optimization, deep learning architectures, neuroimaging, diffeomorphisms, uncertainty, topology and metrics.Table of ContentsAtlases.- Topology.- Uncertainty.- Architectures.- Optimization.- Metrics.- Losses.- Efficiency.
£52.24
Springer International Publishing AG Automated Taxonomy Discovery and Exploration
Book SynopsisThis book provides a principled data-driven framework that progressively constructs, enriches, and applies taxonomies without leveraging massive human annotated data. Traditionally, people construct domain-specific taxonomies by extensive manual curations, which is time-consuming and costly. In today’s information era, people are inundated with the vast amounts of text data. Despite their usefulness, people haven’t yet exploited the full power of taxonomies due to the heavy curation needed for creating and maintaining them. To bridge this gap, the authors discuss automated taxonomy discovery and exploration, with an emphasis on label-efficient machine learning methods and their real-world usages. Taxonomy organizes entities and concepts in a hierarchy way. It is ubiquitous in our daily life, ranging from product taxonomies used by online retailers, topic taxonomies deployed by news outlets and social media, as well as scientific taxonomies deployed by digital libraries across various domains. When properly analyzed, these taxonomies can play a vital role for science, engineering, business intelligence, policy design, e-commerce, and more. Intuitive examples are used throughout enabling readers to grasp concepts more easily.Table of ContentsIntroduction.- Concept Set Expansion.- Taxonomy Construction.- Taxonomy Enrichment.- Taxonomy-Guided Classification.- Conclusions.
£44.99
Springer International Publishing AG Digital Interaction and Machine Intelligence: Proceedings of MIDI’2021 – 9th Machine Intelligence and Digital Interaction Conference, December 9-10, 2021, Warsaw, Poland
Book SynopsisThis book is open access, which means that you have free and unlimited access.This book presents the Proceedings of the 9th Machine Intelligence and Digital Interaction Conference. Significant progress in the development of artificial intelligence (AI) and its wider use in many interactive products are quickly transforming further areas of our life, which results in the emergence of various new social phenomena. Many countries have been making efforts to understand these phenomena and find answers on how to put the development of artificial intelligence on the right track to support the common good of people and societies. These attempts require interdisciplinary actions, covering not only science disciplines involved in the development of artificial intelligence and human-computer interaction but also close cooperation between researchers and practitioners. For this reason, the main goal of the MIDI conference held on 9-10.12.2021 as a virtual event is to integrate two, until recently, independent fields of research in computer science: broadly understood artificial intelligence and human-technology interaction.
£31.49
Springer International Publishing AG Elements of Data Science, Machine Learning, and
Book SynopsisThe textbook provides students with tools they need to analyze complex data using methods from data science, machine learning and artificial intelligence. The authors include both the presentation of methods along with applications using the programming language R, which is the gold standard for analyzing data. The authors cover all three main components of data science: computer science; mathematics and statistics; and domain knowledge. The book presents methods and implementations in R side-by-side, allowing the immediate practical application of the learning concepts. Furthermore, this teaches computational thinking in a natural way. The book includes exercises, case studies, Q&A and examples.Table of Contents1. Introduction2. Introduction to learning from data3. Part 1: General topics4. Prediction models5. Error measures6. Resampling7. Data types8. Part 2: Core methods9. Maximum Likelihood & Bayesian analysis10. Clustering11. Dimension Reduction12. Classification13. Hypothesis testing14. Linear Regression15. Model Selection16. Part 3: Advanced topics17. Regularization18. Deep neural networks19. Multiple hypothesis testing20. Survival analysis21. Generalization error22. Theoretical foundations23. Conclusion.
£49.49
Springer International Publishing AG Econometrics with Machine Learning
Book SynopsisThis book helps and promotes the use of machine learning tools and techniques in econometrics and explains how machine learning can enhance and expand the econometrics toolbox in theory and in practice. Throughout the volume, the authors raise and answer six questions: 1) What are the similarities between existing econometric and machine learning techniques? 2) To what extent can machine learning techniques assist econometric investigation? Specifically, how robust or stable is the prediction from machine learning algorithms given the ever-changing nature of human behavior? 3) Can machine learning techniques assist in testing statistical hypotheses and identifying causal relationships in ‘big data? 4) How can existing econometric techniques be extended by incorporating machine learning concepts? 5) How can new econometric tools and approaches be elaborated on based on machine learning techniques? 6) Is it possible to develop machine learning techniques further and make them even more readily applicable in econometrics?As the data structures in economic and financial data become more complex and models become more sophisticated, the book takes a multidisciplinary approach in developing both disciplines of machine learning and econometrics in conjunction, rather than in isolation. This volume is a must-read for scholars, researchers, students, policy-makers, and practitioners, who are using econometrics in theory or in practice. Table of ContentsLinear Econometric Models with Machine Learning.- Nonlinear Econometric Models with Machine Learning.- The Use of Machine Learning in Treatment Effect Estimation.-Forecasting with Machine Learning Methods.-Causal Estimation of Treatment Effects From Observational Health Care Data Using Machine Learning Methods.- Econometrics of Networks with Machine Learning.- Fairness in Machine Learning and Econometrics.- Graphical Models and their Interactions with Machine Learning in the Context of Economics and Finance.- Poverty, Inequality and Development Studies with Machine Learning.- Machine Learning for Asset Pricing.
£116.99
Springer International Publishing AG Artificial Intelligence, Learning and Computation
Book SynopsisThis book presents frontier research on the use of computational methods to model complex interactions in economics and finance. Artificial Intelligence, Machine Learning and simulations offer effective means of analyzing and learning from large as well as new types of data. These computational tools have permeated various subfields of economics, finance, and also across different schools of economic thought. Through 16 chapters written by pioneers in economics, finance, computer science, psychology, complexity and statistics/econometrics, the book introduces their original research and presents the findings they have yielded.Theoretical and empirical studies featured in this book draw on a variety of approaches such as agent-based modeling, numerical simulations, computable economics, as well as employing tools from artificial intelligence and machine learning algorithms. The use of computational approaches to perform counterfactual thought experiments are also introduced, which help transcend the limits posed by traditional mathematical and statistical tools.The book also includes discussions on methodology, epistemology, history and issues concerning prediction, validation, and inference, all of which have become pertinent with the increasing use of computational approaches in economic analysis.Table of ContentsPerspectives from the Development of Agent-based Modelling in Economics and Finance.- Towards a General Model of Financial Markets.- The U-Mart Futures Exchange Experiment and Her Institutional Design Historically Inherited.- A Bottom-Up Framework for Data-Driven Agent-Based Simulations.- Can News Networks and Topics Influence Assets Return and Volatility?.- Causal Inference and Agent-Based Models.- Finding the Human in Their Stories: Some Thoughts on Digital Humanities Tools.- Interdependence Overcomes the Limitations of Rational Theories of Collective Behavior: The Productivity of Patents by Nations.- Sand Castles and Financial Systems.-Estimation of Agent-Based Models via Approximate Bayesian Computation.- Unravelling Aspects of Decision Making Under Uncertainty.- Logic and Epistemology in Behavioral Economics.- Aggregate Investor Attention and Bitcoin Return: The Machine Learning Approach.- Information and Market Power: An Experimental Investigation into the Hayek Hypothesis.- Algorithmically Learning, Creatively and Intelligently to Play Games.- A Simonian Formalistic Perspective on Collaborative, Distributed Invention.- Modified Sraffan Schemes and Algorithmic Rational Agents.
£104.49
Springer International Publishing AG Machine Learning and Its Application to Reacting Flows: ML and Combustion
Book SynopsisThis open access book introduces and explains machine learning (ML) algorithms and techniques developed for statistical inferences on a complex process or system and their applications to simulations of chemically reacting turbulent flows.These two fields, ML and turbulent combustion, have large body of work and knowledge on their own, and this book brings them together and explain the complexities and challenges involved in applying ML techniques to simulate and study reacting flows. This is important as to the world’s total primary energy supply (TPES), since more than 90% of this supply is through combustion technologies and the non-negligible effects of combustion on environment. Although alternative technologies based on renewable energies are coming up, their shares for the TPES is are less than 5% currently and one needs a complete paradigm shift to replace combustion sources. Whether this is practical or not is entirely a different question, and an answer to this question depends on the respondent. However, a pragmatic analysis suggests that the combustion share to TPES is likely to be more than 70% even by 2070. Hence, it will be prudent to take advantage of ML techniques to improve combustion sciences and technologies so that efficient and “greener” combustion systems that are friendlier to the environment can be designed. The book covers the current state of the art in these two topics and outlines the challenges involved, merits and drawbacks of using ML for turbulent combustion simulations including avenues which can be explored to overcome the challenges. The required mathematical equations and backgrounds are discussed with ample references for readers to find further detail if they wish. This book is unique since there is not any book with similar coverage of topics, ranging from big data analysis and machine learning algorithm to their applications for combustion science and system design for energy generation. Table of ContentsIntroduction.- ML Algorithms, Techniques and their Application to Reactive Molecular Dynamics Simulations.- Big Data Analysis, Analytics & ML role.- ML for SGS Turbulence (including scalar flux) Closures.- ML for Combustion Chemistry.- Applying CNNs to model SGS flame wrinkling in thickened flame LES (TFLES).- Machine Learning Strategy for Subgrid Modelling of Turbulent Combustion using Linear Eddy Mixing based Tabulation.- MILD Combustion–Joint SGS FDF.- Machine Learning for Principal Component Analysis & Transport.- Super Resolution Neural Network for Turbulent non-premixed Combustion.- ML in Thermoacoustics.- Concluding Remarks & Outlook.
£999.99
Springer International Publishing AG Machine Learning Algorithms: Adversarial
Book SynopsisThis book demonstrates the optimal adversarial attacks against several important signal processing algorithms. Through presenting the optimal attacks in wireless sensor networks, array signal processing, principal component analysis, etc, the authors reveal the robustness of the signal processing algorithms against adversarial attacks. Since data quality is crucial in signal processing, the adversary that can poison the data will be a significant threat to signal processing. Therefore, it is necessary and urgent to investigate the behavior of machine learning algorithms in signal processing under adversarial attacks. The authors in this book mainly examine the adversarial robustness of three commonly used machine learning algorithms in signal processing respectively: linear regression, LASSO-based feature selection, and principal component analysis (PCA). As to linear regression, the authors derive the optimal poisoning data sample and the optimal feature modifications, and also demonstrate the effectiveness of the attack against a wireless distributed learning system. The authors further extend the linear regression to LASSO-based feature selection and study the best strategy to mislead the learning system to select the wrong features. The authors find the optimal attack strategy by solving a bi-level optimization problem and also illustrate how this attack influences array signal processing and weather data analysis. In the end, the authors consider the adversarial robustness of the subspace learning problem. The authors examine the optimal modification strategy under the energy constraints to delude the PCA-based subspace learning algorithm. This book targets researchers working in machine learning, electronic information, and information theory as well as advanced-level students studying these subjects. R&D engineers who are working in machine learning, adversarial machine learning, robust machine learning, and technical consultants working on the security and robustness of machine learning are likely to purchase this book as a reference guide.Table of ContentsChapter. 1. IntroductionChapter. 2. Optimal Feature Manipulation Attacks Against Linear RegressionChapter. 3. On the Adversarial Robustness of LASSO Based Feature SelectionChapter. 4. On the Adversarial Robustness of Subspace LearningChapter. 5. Summary and ExtensionsChapter. 6. Appendix
£98.99
Springer International Publishing AG Medical Image Computing and Computer Assisted
Book SynopsisThe eight-volume set LNCS 13431, 13432, 13433, 13434, 13435, 13436, 13437, and 13438 constitutes the refereed proceedings of the 25th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2022, which was held in Singapore in September 2022. The 574 revised full papers presented were carefully reviewed and selected from 1831 submissions in a double-blind review process. The papers are organized in the following topical sections: Part I: Brain development and atlases; DWI and tractography; functional brain networks; neuroimaging; heart and lung imaging; dermatology; Part II: Computational (integrative) pathology; computational anatomy and physiology; ophthalmology; fetal imaging; Part III: Breast imaging; colonoscopy; computer aided diagnosis; Part IV: Microscopic image analysis; positron emission tomography; ultrasound imaging; video data analysis; image segmentation I; Part V: Image segmentation II; integration of imaging with non-imaging biomarkers; Part VI: Image registration; image reconstruction; Part VII: Image-Guided interventions and surgery; outcome and disease prediction; surgical data science; surgical planning and simulation; machine learning – domain adaptation and generalization; Part VIII: Machine learning – weakly-supervised learning; machine learning – model interpretation; machine learning – uncertainty; machine learning theory and methodologies. Table of ContentsBrain Development and Atlases.- Progression models for imaging data with Longitudinal Variational Auto Encoders.- Boundary-Enhanced Self-Supervised Learning for Brain Structure Segmentation.- Domain-Prior-Induced Structural MRI Adaptation for Clinical Progression Prediction of Subjective Cognitive Decline.- 3D Global Fourier Network for Alzheimer’s Disease Diagnosis using Structural MRI.- CASHformer: Cognition Aware SHape Transformer for Longitudinal Analysis.- Interpretable differential diagnosis for Alzheimer’s disease and Frontotemporal dementia.- Is a PET all you need? A multi-modal study for Alzheimer’s disease using 3D CNNs.- Unsupervised Representation Learning of Cingulate Cortical Folding Patterns.- Feature robustness and sex differences in medical imaging: a case study in MRI-based Alzheimer’s disease detection.- Extended Electrophysiological Source Imaging with Spatial Graph Filters.- DWI and Tractography.- Hybrid Graph Transformer for Tissue Microstructure Estimation with Undersampled Diffusion MRI Data.- Atlas-powered deep learning (ADL) - application to diffusion weighted MRI.- One-Shot Segmentation of Novel White Matter Tracts via Extensive Data Augmentation.- Accurate Corresponding Fiber Tract Segmentation via FiberGeoMap Learner.- An adaptive network with extragradient for diffusion MRI-based microstructure estimation.- Shape-based features of white matter fiber-tracts associated with outcome in Major Depression Disorder.- White Matter Tracts are Point Clouds: Neuropsychological Score Prediction and Critical Region Localization via Geometric Deep Learning.- Segmentation of Whole-brain Tractography: A Deep Learning Algorithm Based on 3D Raw Curve Points.- TractoFormer: A Novel Fiber-level Whole Brain Tractography Analysis Framework Using Spectral Embedding and Vision Transformers.- Multi-site Normative Modeling of Diffusion Tensor Imaging Metrics Using Hierarchical Bayesian Regression.- Functional Brain Networks.- Contrastive Functional Connectivity Graph Learning for Population-based fMRI Classification.- Joint Graph Convolution for Analyzing Brain Structural and Functional Connectome.- Decoding Task Sub-type States with Group Deep Bidirectional Recurrent Neural Network.- Hierarchical Brain Networks Decomposition via Prior Knowledge Guided Deep Belief Network.- Interpretable signature of consciousness in resting-state functional network brain activity.- Nonlinear Conditional Time-varying Granger Causality of Task fMRI via Deep Stacking Networks and Adaptive Convolutional Kernels.- fMRI Neurofeedback Learning Patterns are Predictive of Personal and Clinical Traits.- Multi-head Attention-based Masked Sequence Model for Mapping Functional Brain Networks.- Dual-HINet: Dual Hierarchical Integration Network of Multigraphs for Connectional Brain Template Learning.- RefineNet: An Automated Framework to Generate Task and Subject-Specific Brain Parcellations for Resting-State fMRI Analysis.- Modelling Cycles in Brain Networks with the Hodge Laplacian.- Predicting Spatio-Temporal Human Brain Response Using fMRI.- Revealing Continuous Brain Dynamical Organization with Multimodal Graph Transformer.- Explainable Contrastive Multiview Graph Representation of Brain, Mind, and Behavior.- Embedding Human Brain Function via Transformer.- How Much to Aggregate: Learning Adaptive Node-wise Scales on Graphs for Brain Networks.- Combining multiple atlases to estimate data-driven mappings between functional connectomes using optimal transport.- The Semi-constrained Network-Based Statistic (scNBS): integrating local and global information for brain network inference.- Unified Embeddings of Structural and Functional Connectome via a Function-Constrained Structural Graph Variational Auto-Encoder.- Neuroimaging.- Characterization of brain activity patterns across states of consciousness based on variational auto-encoders.- Conditional VAEs for confound removal and normative modelling of neurodegenerative diseases.- Semi-supervised learning with data harmonisation for biomarker discovery from resting state fMRI.- Cerebral Microbleeds Detection Using a 3D Feature Fused Region Proposal Network with Hard Sample Prototype Learning.- Brain-Aware Replacements for Supervised Contrastive Learning in Detection of Alzheimer’s Disease.- Heart and Lung Imaging.- AANet: Artery-Aware Network for Pulmonary Embolism Detection in CTPA Images.- Siamese Encoder-based Spatial-Temporal Mixer for Growth Trend Prediction of Lung Nodules on CT Scans.- What Makes for Automatic Reconstruction of Pulmonary Segments.- CFDA: Collaborative Feature Disentanglement and Augmentation for Pulmonary Airway Tree Modeling of COVID-19 CTs.- Decoupling Predictions in Distributed Learning for Multi-Center Left Atrial MRI Segmentation.- Scribble-Supervised Medical Image Segmentation via Dual-Branch Network and Dynamically Mixed Pseudo Labels Supervision.- Diffusion Deformable Model for 4D Temporal Medical Image Generation.- SAPJNet: Sequence-Adaptive Prototype-Joint Network for Small Sample Multi-Sequence MRI Diagnosis.- Evolutionary Multi-objective Architecture Search Framework: Application to COVID-19 3D CT Classification.- Detecting Aortic Valve Pathology from the 3-Chamber Cine Cardiac MRI View.- CheXRelNet: An Anatomy-Aware Model for Tracking Longitudinal Relationships between Chest X-Rays.- Reinforcement learning for active modality selection during diagnosis.- Ensembled Prediction of Rheumatic Heart Disease from Ungated Doppler Echocardiography Acquired in Low-Resource Settings.- Attention mechanisms for physiological signal deep learning: which attention should we take?.- Computer-aided Tuberculosis Diagnosis with Attribute Reasoning Assistance.- Multimodal Contrastive Learning for Prospective Personalized Estimation of CT Organ Dose.- RTN: Reinforced Transformer Network for Coronary CT Angiography Vessel-level Image Quality Assessment.- A Comprehensive Study of Modern Architectures and Regularization Approaches on CheXpert5000.- LSSANet: A Long Short Slice-Aware Network for Pulmonary Nodule Detection.- Consistency-based Semi-supervised Evidential Active Learning for Diagnostic Radiograph Classification.- Self-Rating Curriculum Learning for Localization and Segmentation of Tuberculosis on Chest Radiograph.- Rib Suppression in Digital Chest Tomosynthesis.- Multi-Task Lung Nodule Detection in Chest Radiographs with a Dual Head Network.- Dermatology.- Data-Driven Deep Supervision for Skin Lesion Classification.- Out-of-Distribution Detection for Long-tailed and Fine-grained Skin Lesion Images.- FairPrune: Achieving Fairness Through Pruning for Dermatological Disease Diagnosis.- Reliability-aware Contrastive Self-ensembling for Semi-supervised Medical Image Classification.
£42.74
Springer International Publishing AG Domain Adaptation and Representation Transfer:
Book SynopsisThis book constitutes the refereed proceedings of the 4th MICCAI Workshop on Domain Adaptation and Representation Transfer, DART 2022, held in conjunction with MICCAI 2022, in September 2022. DART 2022 accepted 13 papers from the 25 submissions received. The workshop aims at creating a discussion forum to compare, evaluate, and discuss methodological advancements and ideas that can improve the applicability of machine learning (ML)/deep learning (DL) approaches to clinical setting by making them robust and consistent across different domains. Table of ContentsDetecting Melanoma Fairly: Skin Tone Detection and Debiasing for Skin Lesion Classification.- Benchmarking Transformers for Medical Image Classification.- Supervised domain adaptation using gradients transfer for improved medical image analysis.- Stain-AgLr: Stain Agnostic Learning for Computational Histopathology using Domain Consistency and Stain Regeneration Loss.- MetaMedSeg: Volumetric Meta-learning for Few-Shot Organ Segmentation.- Unsupervised site adaptation by intra-site variability alignment.- Discriminative, Restorative, and Adversarial Learning: Stepwise Incremental Pretraining.- POPAR: Patch Order Prediction and Appearance Recovery for Self-supervised Medical Image Analysis.- Feather-Light Fourier Domain Adaptation in Magnetic Resonance Imaging.- Seamless Iterative Semi-Supervised Correction of Imperfect Labels in Microscopy Images.- Task-agnostic Continual Hippocampus Segmentation for Smooth Population Shifts.- Adaptive Optimization with Fewer Epochs Improves Across-Scanner Generalization of U-Net based Medical Image Segmentation.- CateNorm: Categorical Normalization for Robust Medical Image Segmentation.
£42.74
Springer International Publishing AG Computational Mathematics Modeling in Cancer
Book SynopsisThis book constitutes the proceedings of the First Workshop on Computational Mathematics Modeling in Cancer Analysis (CMMCA2022), held in conjunction with MICCAI 2022, in Singapore in September 2022. Due to the COVID-19 pandemic restrictions, the CMMCA2022 was held virtually. DALI 2022 accepted 15 papers from the 16 submissions that were reviewed. A major focus of CMMCA2022 is to identify new cutting-edge techniques and their applications in cancer data analysis in response to trends and challenges in theoretical, computational and applied aspects of mathematics in cancer data analysis.Table of ContentsCellular Architecture on Whole Slide Images Allows the Prediction of Survival in Lung Adenocarcinoma .- Is More Always Better? Effects of Patch Sampling in Distinguishing Chronic Lymphocytic Leukemia from Transformation to Diffuse Large B-cell Lymphoma.- Repeatability of Radiomic Features against Simulated Scanning Position Stochasticity across Imaging Modalities and Cancer Subtypes: A Retrospective Multi-Institutional Study on Head-and-Neck Cases.- MLCN: Metric Learning Constrained Network for Whole Slide Image Classification with Bilinear Gated Attention Mechanism.- NucDETR: End-to-End Transformer for Nucleus Detection in Histopathology Images.- Self-supervised learning based on a pre-trained method for the subtype classification of spinal tumors.- CanDLE: Illuminating Biases in Transcriptomic Pan-Cancer Diagnosis.- Cross-Stream Interactions: Segmentation of Lung Adenocarcinoma Growth Patterns.- Modality-collaborative AI model Ensemble for Lung Cancer Early Diagnosis.- Clustering-based Multi-instance Learning Network for Whole Slide Image Classification.- Multi-task Learning-driven Volume and Slice Level Contrastive Learning for 3D Medical Image Classification.- Light Annotation Fine Segmentation: Histology Image Segmentation based on VGG Fusion with Global Normalisation CAM.- Tubular Structure-Aware Convolutional Neural Networks for Organ at Risks Segmentation in Cervical Cancer Radiotherapy.- Automatic Computer-aided Histopathologic Segmentation for Nasopharyngeal Carcinoma using Transformer Framework.- Accurate Breast Tumor Identification UsingComputational Ultrasound Image Features.
£42.74
