Pattern recognition Books
Springer International Publishing AG Pattern Recognition: 15th Mexican Conference,
Book SynopsisThis book constitutes the refereed proceedings of the 15th Mexican Conference on Pattern Recognition, MCPR 2023, held in Tepic, Mexico, during June 21–24, 2023.The 30 full papers presented in this book were carefully reviewed and selected from 61 submissions. The papers are divided into the following topical sections: pattern recognition and machine learning techniques; deep learning and neural networks; medical applications of pattern recognition; language processing and recognition; and industrial applications of pattern recognition.Table of ContentsPattern Recognition and Machine Learning Techniques: Feature Analysis and Selection for Water Stream Modeling.- A Cloud-based (AWS) Machine Learning Solution to Predict Account Receivables in a Financial Institution.- A New Approach for Road Type Classification using Multi-Stage Graph Embedding Method.- Removing the Black-Box from Machine Learning.- Using Machine Learning to Identify Patterns in Learner-Submitted Code for the Purpose of Assessment.- Fitness Function Comparison for Unsupervised Feature Selection with Permutational-Based Dierential Evolution.- A Method for Counting Models on Cubic Boolean Formulas.- Automatic Identication of Learning Styles through Behavioral Patterns.- Comparison of Classiers in Challenge Scheme.- Deep Learning and Neural Networks: Robust Zero-Watermarking for Medical Images based on Deep Learning Feature Extraction.- Plant Stress Recognition Using Deep Learning and 3D Reconstruction.- Segmentation and Classification Networks for Corn/Weed Detection under Excessive Field Variabilities.- Leukocyte Recognition Using a Modified AlexNet and Image to Image GAN Data Augmentation.- Spoofing Detection for Speaker Verification with Glottal Flow and 1D Pure Convolutional Networks.- Estimation of Stokes Parameters using Deep Neural Networks.- Experimental Study of the Performance of Convolutional Neural Networks Applied in Art Media Classification.- Medical Applications of Pattern Recognition: Hadamard Layer to Improve Semantic Segmentation in Medical Images.- Patterns in Genesis of Breast Cancer Tumor.- Realistic Simulation of Event-Related Potentials and their usual Noise and Interferences for Pattern Recognition.- Chest X-ray Imaging Severity Score of COVID-19 Pneumonia.- Leukocyte Detection with Novel Fully Convolutional Network and a New Dataset of Blood Smear Complete Samples.- Comparison of Deep Learning Architectures in Classification of Microcalcifications Clusters in Digital Mammograms.- Retinal Artery and Vein Segmentation using an Image-to-image Conditional Adversarial Network.- Evaluation of Heatmaps as an Explicative Method for Classifying Acute Lymphoblastic Leukemia Cells.- Language Processing and Recognition: Machine Learning Models Applied in Sign Language Recognition.- Urdu Semantic Parsing: An Improved SEMPRE Framework for Conversion of Urdu Language Web Queries to Logical forms.- Improving the Identification of Abusive Language through Careful Design of Pre-training Tasks.- Industrial Applications of Pattern Recognition: TOPSIS Method for Multiple-Criteria Decision-Making Applied to Trajectory Selection for Autonomous Driving.- Machine-learning based Estimation of the Bending Magnitude Sensed by a Fiber Optic Device.- Graph-based Semi-Supervised Learning using Riemannian Geometry Distance for Motor Imagery Classification.
£56.99
Springer International Publishing AG Ophthalmic Medical Image Analysis: 10th
Book SynopsisThis book constitutes the refereed proceedings of the 10th International Workshop on Ophthalmic Medical Image Analysis, OMIA 2023, held in conjunction with the 26th International Conference on Medical Imaging and Computer-Assisted Intervention, MICCAI 2023, in Vancouver, Canada, in October 2023.The 16 papers presented at OMIA 2023 were carefully reviewed and selected from 27 submissions. The papers cover various topics in the field of ophthalmic medical image analysis and challenges in terms of reliability and validation, number and type of conditions considered, multi-modal analysis (e.g., fundus, optical coherence tomography, scanning laser ophthalmoscopy), novel imaging technologies, and the effective transfer of advanced computer vision and machine learning technologies.
£75.99
Springer International Publishing AG Advanced Concepts for Intelligent Vision Systems:
Book SynopsisThis book constitutes the proceedings of the 21st International Conference on Advanced Concepts for Intelligent Vision Systems, ACIVS 2023, held in Kumamoto, Japan, during August 2023. The 31 papers presented in this volume were carefully reviewed and selected from a total of 48 submissions. They were organized in topical sections named: Computer Vision, Affective Computing and Human Interactions, Managing the Biodiversity, Robotics and Drones, Machine Learning.Table of ContentsA hybrid quantum-classical segment-based Stereo Matching algorithm.- Continuous Exposure for Extreme Low-Light Imaging.- Semi-supervised Classification and Segmentation of Forest Fire using Autoencoders.- Descriptive and coherent paragraph generation for image paragraph captioning using vision transformer and post-processing.- Pyramid Swin Transformer for Multi-Task: Expanding to more computer vision tasks.- Person activity classification from an aerial sensor based on a multi-level deep features.- Person Quick-Search Approach based on a Facial Semantic Attributes Description.- Age-Invariant Face Recognition using Face Feature Vectors and Embedded Prototype Subspace Classifiers.- BENet: A lightweight bottom-up framework for context-aware emotion recognition.- Yolopoint: Joint Keypoint and Object Detection.- Less-than-one shot 3d segmentation hijacking a pre-trained space-time memory network.- Segmentation of Range-Azimuth Maps of FMCW radars with a deep convolutional neural network.- Segmentation of Range-Azimuth Maps of FMCW radars with a deep convolutional neural network.- A Single Image Neuro-Geometric Depth Estimation.- Wave-shaping Neural Activation for Improved 3D Model Reconstruction from Sparse Point Clouds.- A Deep Learning Approach to Segment High-Content Images of the E.coli Bacteria.- Multimodal Emotion Recognition System Through Three Different Channels (MER-3C).- Multi-Modal Obstacle Avoidance in USVs via Anomaly Detection and Cascaded Datasets.- A Contrario Mosaic Analysis for Image Forensics.- IRIS SEGMENTATION TECHNIQUE USING IRIS-UNet METHOD.- Image Acquisition by Image Retrieval with Color Aesthetics.- Improved Obstructed Facial Feature Reconstruction for Emotion Recognition with Minimal Change CycleGANs.- Quality assessment for high dynamic range stereoscopic omnidirectional image system.- Genetic Programming with Convolutional Operators for Albatross Nest Detection from Satellite Imaging.- Reinforcement Learning for truck Eco-driving: a serious game as driving assistance system.- Underwater mussel segmentation using smoothed shape descriptors with random forest.- A 2D Cortical Flat Map Space for Computationally Efficient Mammalian Brain simulation.- Construction of a novel data set for pedestrian tree species detection using google street view data.- Texture-based Data Augmentation for Small Datasets.- Multimodal Representations for Teacher-Guided Compositional Visual Reasoning.- Enhanced Color QR Codes with Resilient Error Correction for Dirt-Prone Surfaces.
£56.99
Springer International Publishing AG Image and Graphics: 12th International
Book SynopsisThe five-volume set LNCS 14355, 14356, 14357, 14358 and 14359 constitutes the refereed proceedings of the 12th International Conference on Image and Graphics, ICIG 2023, held in Nanjing, China, during September 22–24, 2023.The 166 papers presented in the proceedings set were carefully reviewed and selected from 409 submissions. They were organized in topical sections as follows: computer vision and pattern recognition; computer graphics and visualization; compression, transmission, retrieval; artificial intelligence; biological and medical image processing; color and multispectral processing; computational imaging; multi-view and stereoscopic processing; multimedia security; surveillance and remote sensing, and virtual reality.The ICIG 2023 is a biennial conference that focuses on innovative technologies of image, video and graphics processing and fostering innovation, entrepreneurship, and networking. It will feature world-class plenary speakers, exhibits, and high quality peer reviewed oral and poster presentations. Table of ContentsComputer Vision and Pattern Recognition: Temporal Global Re-Detection Based on Interaction-Fusion Attention in Long-Term Visual Tracking.- VLNet: A Multi-task Network for Joint Vehicle and Lane Detection.- Adaptive Cost Aggregation in Iterative Depth Estimation for Efficient Multi-View Stereo.- A Novel Semantic Segmentation Method for High-Resolution Remote Sensing Images Based on Visual Attention Network.- Efficient Few-shot Image Generation via Lightweight Octave Generative Adversarial Networks.- Incorporating Global Correlation and Local Aggregation for Efficient Visual Localization.- Deep Interactive Image Semantic and Instance Segmentation.- Learning High-Performance Spiking Neural Networks With Multi-Compartment Spiking Neurons.- Attribute Space Analysis for Image Editing.-SAGAN: Self-Attention Generative Adversarial Net-work for RGB-D Saliency Prediction.- Behavioural State Detection Algorithm for Infants and Toddlers Incorporating Multi-scale Contextual Features.- Motion-Scenario Decoupling for Rat-Aware Video Position Prediction: Strategy and Benchmark.- Dual Fusion Network for Hyperspectral Semantic Segmentation.- Strip-FFT Transformer for single image deblurring.- Vision-Language Adaptive Mutual Decoder for OOV-STR.- DensityLayout: Density-conditioned Layout GAN for Visual-textual Presentation Designs.- GLTCM: Global-Local Temporal and Cross-Modal Network for Audio-Visual Event Localization.- Recent Advances in Class-Incremental Learning.- TTA-GCN: Temporal Topology Aggregation for Skeleton-Based Action Recognition.- A Stable Long-Term Tracking Method For Group-Housed Pigs.- Dynamic Attention for Isolated Sign Language Recognition with Reinforcement Learning.- A Segmentation Method based on SE Attention and U-Net for Apple Image.- Human Action Recognition Method based on spatio-temporal relationship.- Facial expression recognition from occluded images using deep convolution neural network with Vision Transformer.- Learning Discriminative Proposal Representation for Multi-Object Tracking.- Virtual-Violence: A Brand-New Dataset for Video Violence Recognition.- A novel Attention-DeblurGAN-Based Defogging Algorithm.- Multi-Modal Context-Aware Network for Scene Graph Generation.- VQA-CLPR: Turning a Visual Question Answering Model into a Chinese License Plate Recognizer.- Unsupervised Vehicle Re-Identification via Raw UAV Videos.- Distance-Aware Vector-Field and Vector Screening Strategy for 6D Object Pose Estimation.- SSTA-Net: Self-supervised Spatio-Temporal Attention Network for Action Recognition.- Gesture recognition method based on Sim-ConvNeXt model.- Research on Airborne Infrared Target Recognition Method based on Target-Environment Coupling.- Semantic and Gradient Guided Scene Text Image Super-Resolution.
£61.74
Springer International Publishing AG Image and Graphics: 12th International
Book SynopsisThe five-volume set LNCS 14355, 14356, 14357, 14358 and 14359 constitutes the refereed proceedings of the 12th International Conference on Image and Graphics, ICIG 2023, held in Nanjing, China, during September 22–24, 2023.The 166 papers presented in the proceedings set were carefully reviewed and selected from 409 submissions. They were organized in topical sections as follows: computer vision and pattern recognition; computer graphics and visualization; compression, transmission, retrieval; artificial intelligence; biological and medical image processing; color and multispectral processing; computational imaging; multi-view and stereoscopic processing; multimedia security; surveillance and remote sensing, and virtual reality.The ICIG 2023 is a biennial conference that focuses on innovative technologies of image, video and graphics processing and fostering innovation, entrepreneurship, and networking. It will feature world-class plenary speakers, exhibits, and high-quality peer reviewed oral and poster presentations.Table of ContentsBiological and Medical Image Processing: An efficient medical image fusion via online convolutional sparse coding with sample-dependent dictionary.- Multimodal Medical Image Fusion based on Multichannel Aggregated Network.- Multi-Path Feature Fusion and Channel Feature Pyramid for Brain Tumor Segmentation in MRI.- Near infrared video heart rate detection based on multi-region selection and robust principal component analysis- Neural video: A novel framework for interpreting the spatiotemporal activities of the human brain.- Local Fusion Synthetic CT Network for Improving the Quality of CBCT in Cervical Cancer Radiotherapy.- Causality-Inspired Source-Free Domain Adaptation for Medical Image Classification.- Pixel-Correlation-Based Scar Screening in Hypertrophic Myocardium.- Dictionary Matching Based 2D Thin Slice Generalized Slice-dithered Enhanced Resolution (gSlider) Variable Flip Angle T1 Mapping.- Deep Low-rank Multimodal Fusion with Inter-modal Distribution Difference Constraint for ASD Diagnosis.- Cross-domain Tongue Image Segmentation based on Deep Adversarial Networks and Entropy Minimization.- Residual Inter-slice Feature Learning for 3D Organ Segmentation.- Color and Multispectral Processing: A Cross-paired Wavelet Based Spatiotemporal Fusion Network for Remote Sensing Images.- Coupled Dense Convolutional Neural Networks with Autoencoder For Unsupervised Hyperspectral Super Resolution.- Computational Imaging: Multi-scale Non-Local Bidirectional Fusion for Video Super-Resolution.- Multi-View and Stereoscopic Processing: Learning a Deep Fourier Channel Attention Generative Adversarial Network for Light Field Image Super-Resolution.- Synthesizing a large scene with multiple NeRFs.- Neural Implicit 3D Shapes from Single Images with Spatial Patterns.- Multimedia Security: Dense Visible Watermark Removal with Progressive Feature Propagation.- When Diffusion Model Meets with Adversarial Attack: Generating Transferable Adversarial Examples on Face Recognition.- TPM: Two-stage prediction mechanism for universal adversarial patch defense.- Surveillance and remote sensing: WSAD-Net: Weakly Supervised Anomaly Detection in Untrimmed Surveillance Videos.- Multi-object Tracking in Remote Sensing Video Based on Motion and Multi-scale Local Cost Volume.- Density Map Augmentation-based Point-to-point Vehicle Counting and Localization in Remote Sensing Imagery with Limited Resolution.- Large Window Attention based Transformer Network for Change Detection of Remote Sensing Images.- U-TEN:An Unsupervised Two-branch Enhancement Network for Object Detection under Complex-Light Condition.- Virtual Reality: Design of virtual assembly guidance system based on mixed reality.- Blind Omnidirectional Image Quality Assessment Based on Swin Transformer with Scanpath-Oriented.- Audio-visual Saliency for Omnidirectional Videos.
£56.99
Springer International Publishing AG Pattern Recognition: 7th Asian Conference, ACPR 2023, Kitakyushu, Japan, November 5–8, 2023, Proceedings, Part I
Book SynopsisThis three-volume set LNCS 14406-14408 constitutes the refereed proceedings of the 7th Asian Conference on Pattern Recognition, ACPR 2023, held in Kitakyushu, Japan, in November 2023. The 93 full papers presented were carefully reviewed and selected from 164 submissions. The conference focuses on four important areas of pattern recognition: pattern recognition and machine learning, computer vision and robot vision, signal processing, and media processing and interaction, covering various technical aspects.Table of Contentsartificial intelligence.- computer networks.- computer science.- computer systems.- computer vision.- databases.- education.- engineering.- image analysis.- image processing.- image segmentation.- internet learning.- machine learning.- mathematics.- neural networks.- object recognition.- pattern recognition semantics.- signal processing.
£61.74
Springer International Publishing AG Pattern Recognition: 7th Asian Conference, ACPR
Book SynopsisThis three-volume set LNCS 14406-14408 constitutes the refereed proceedings of the 7th Asian Conference on Pattern Recognition, ACPR 2023, held in Kitakyushu, Japan, in November 2023. The 93 full papers presented were carefully reviewed and selected from 164 submissions. The conference focuses on four important areas of pattern recognition: pattern recognition and machine learning, computer vision and robot vision, signal processing, and media processing and interaction, covering various technical aspects.Table of Contentsartificial intelligence.- computer networks.- computer science.- computer systems.- computer vision.- databases.- education.- engineering.- image analysis.- image processing.- image segmentation.- internet learning.- machine learning.- mathematics.- neural networks.- object recognition.- pattern recognition semantics.- signal processing.
£61.74
Springer International Publishing AG Pattern Recognition: 7th Asian Conference, ACPR
Book SynopsisThis three-volume set LNCS 14406-14408 constitutes the refereed proceedings of the 7th Asian Conference on Pattern Recognition, ACPR 2023, held in Kitakyushu, Japan, in November 2023. The 93 full papers presented were carefully reviewed and selected from 164 submissions. The conference focuses on four important areas of pattern recognition: pattern recognition and machine learning, computer vision and robot vision, signal processing, and media processing and interaction, covering various technical aspects.Table of Contentsartificial intelligence.- computer networks.- computer science.- computer systems.- computer vision.- databases.- education.- engineering.- image analysis.- image processing.- image segmentation.- internet learning.- machine learning.- mathematics.- neural networks.- object recognition.- pattern recognition semantics.- signal processing.
£56.99
Springer International Publishing AG Continuous Biometric Authentication Systems: An
Book SynopsisThis book offers an overview of the field of continuous biometric authentication systems, which capture and continuously authenticate biometrics from user devices. This book first covers the traditional methods of user authentication and discusses how such techniques have become cumbersome in the world of mobile devices and short usage sessions. The concept of continuous biometric authentication systems is introduced and their construction is discussed. The different biometrics that these systems may utilise (e.g.: touchscreen-gesture interactions) are described and relevant studies surveyed. It also surveys important considerations and challenges.This book brings together a wide variety of key motivations, components and advantages of continuous biometric authentication systems. The overview is kept high level, so as not to limit the scope to any single device, biometric trait, use-case, or scenario. Therefore, the contents of this book are applicable to devices ranging from smartphones to desktop computers, utilising biometrics ranging from face recognition to keystroke dynamics. It also provides metrics from a variety of existing systems such that users can identify the advantages and disadvantages of different approaches.This book targets researchers and lecturers working in authentication, as well as advanced-level students in computer science interested in this field. The book will also be of interest to technical professionals working in cyber security.Table of ContentsIntroduction.- Traditional Authentication.- Continuous Authentication.- Biometrics for Continuous Authentication.- Considerations and Challenges.- Conclusion.
£37.99
Springer-Verlag GmbH Image Processing and Vision Engineering
£66.00
Springer Pattern Recognition Applications and Methods
Book SynopsisTheory and Methods.- ShapeAug++: More Realistic Shape Augmentation for Event Data.- Heterogeneous Transfer Learning in Sports: Human Action Recognitionfor Gender and Outcome Prediction.- Evolutionary Training Set Pruning for Boosting Interpolation Kernel Machines.- XPCA Gen: Extended PCA Based Tabular Data Generation Model with Regularisation.- Applications.- Fast Filtering by Conjunctive Enumeration of Sketches for Nearest Neighbor Search.- Bilingual Knowledge Management System for Information Retrieval from Military Policies.- Semantically aware SSM-VPR for Optimal Deep Visual Place Recognition in Waterborne Domains.- Tree Ring Detection for Raw Wood Cross-Section Image Analysis.- Interpreting Cattle Behaviour and Specific Behaviour Intensities with Acoustic Biomarkers.- Towards Objective Assessment of Cleft Surgical Outcomes: A GAN-Inpainting Based Approach.
£47.49
De Gruyter Imaging Science
Book Synopsis
£129.67
Springer Fachmedien Wiesbaden Bildregistrierung für die navigierte Chirurgie:
Book SynopsisDie einzig anerkannte, kurative Therapieform zur Behandlung primärer und sekundärer Lebertumore ist die chirurgische Resektion. Nicht alle Tumore sind aufgrund ihrer Lage resektabel. Eine Navigationsunterstützung kann hier Abhilfe leisten. Exakte Bildregistrierungsverfahren für die Registrierung von Gefäßbäumen aus CT und Ultraschall sind dazu jedoch unerlässlich. Janine Olesch stellt Verfahren vor, mit deren Hilfe im intra-operativen Kontext eine Nachführung der Planungsdaten an die intra-operative Situation gelingt. Neben der Technik der Registrierung mit inexakten Landmarken beschreibt die Autorin auch das Konzept der 2D-3D-Registrierung. Zur methodischen Beschleunigung der Verfahren kommt eine Fokussierungsstrategie zur Registrierung besonders interessanter Regionen zum Einsatz.Table of ContentsProblemstellung Navigierte Leberchirurgie.- Medizintechnische Grundlagen.- Grundlagen der numerischen Optimierung.- Grundlagen der Bildregistrierung.- Spezialisierte Registrierungsansätze.- Anwendungen in der navigierten Leberchirurgie.
£44.99
Springer Fachmedien Wiesbaden Unsupervised Pattern Discovery in Automotive Time
Book SynopsisIn the last decade unsupervised pattern discovery in time series, i.e. the problem of finding recurrent similar subsequences in long multivariate time series without the need of querying subsequences, has earned more and more attention in research and industry. Pattern discovery was already successfully applied to various areas like seismology, medicine, robotics or music. Until now an application to automotive time series has not been investigated. This dissertation fills this desideratum by studying the special characteristics of vehicle sensor logs and proposing an appropriate approach for pattern discovery. To prove the benefit of pattern discovery methods in automotive applications, the algorithm is applied to construct representative driving cycles. Table of ContentsIntroduction.- RelatedWork.- Development of Pattern Discovery Algorithms for Automotive Time Series.- Pattern-based Representative Cycles.- Evaluation.- Conclusion.
£71.99
Springer Fachmedien Wiesbaden Bildverarbeitung für die Medizin 2022:
Book SynopsisIn den letzten Jahren hat sich der Workshop "Bildverarbeitung für die Medizin" durch erfolgreiche Veranstaltungen etabliert. Ziel ist auch 2022 wieder die Darstellung aktueller Forschungsergebnisse und die Vertiefung der Gespräche zwischen Wissenschaftlern, Industrie und Anwendern. Die Beiträge dieses Bandes - einige davon in englischer Sprache - umfassen alle Bereiche der medizinischen Bildverarbeitung, insbesondere Bildgebung und -akquisition, Maschinelles Lernen, Bildsegmentierung und Bildanalyse, Visualisierung und Animation, Zeitreihenanalyse, Computerunterstützte Diagnose, Biomechanische Modellierung, Validierung und Qualitätssicherung, Bildverarbeitung in der Telemedizin u.v.m.
£58.49
Springer Fachmedien Wiesbaden Computergrafik und Bildverarbeitung: Band I:
Book SynopsisIm Buch „Computergrafik und Bildverarbeitung“ finden Sie alles, was Sie für Studium und Praxis über Generierung und Verarbeitung von digitalen Bildern wissen möchten und wie Sie es anwenden. Das erfolgreiche didaktische Konzept wurde weiterentwickelt und liegt ab dieser dritten Auflage in zwei Teilen vor. „Computergrafik und Bildverarbeitung“ Band I führt den Leser durch die Themen der Computergrafik. Dabei werden das alte und neue OpenGL parallel dargestellt, um einen guten Zugang für Einsteiger und einen leichteren Übergang für Fortgeschrittene zu gewährleisten. Profitieren Sie von dem kostenlosen Online-Service: Bildverarbeitungswerkzeuge, Beispiel-Software und interaktive Vorlesungen (als HTML-Seiten mit Java-Applets und Praktikumsaufgaben).Trade Review"'Computergrafik und Bildverarbeitung' ist bestens für Informatik-Studenten oder Praktiker geeignet. Beide Themen werden in einfacher Sprache, aber auch mit Mathematik erklärt."www.it-rezensionen.de, 20.08.2007"Das Werk ist eine anspruchsvolle, aber verständlich geschriebene und didaktisch gut gemachte Darstellung der beiden Bereiche [Computergrafik und Bildbearbeitung]."ekz-Informationsdienst, ID 18/07.Table of ContentsInteraktive 3D-Computergrafik – OpenGL – Geometrische Grundobjekte – Koordinatensysteme und Transformationen – Verdeckung – Farben - Beleuchtungsmodelle – Textur-Mapping – Schatten – Szenengraphen – Cull-Algorithmen – GPU Programmierung mit CUDA und OpenCL
£37.99
Springer Verlag, Singapore Computational Intelligence in Pattern
Book SynopsisThis book features high-quality research papers presented at the 3rd International Conference on Computational Intelligence in Pattern Recognition (CIPR 2021), held at the Institute of Engineering and Management, Kolkata, West Bengal, India, on 24 – 25 April 2021. It includes practical development experiences in various areas of data analysis and pattern recognition, focusing on soft computing technologies, clustering and classification algorithms, rough set and fuzzy set theory, evolutionary computations, neural science and neural network systems, image processing, combinatorial pattern matching, social network analysis, audio and video data analysis, data mining in dynamic environments, bioinformatics, hybrid computing, big data analytics and deep learning. It also provides innovative solutions to the challenges in these areas and discusses recent developments.Table of ContentsApplication of Big Data and Machine Learning for Astrological Predictions.- A Comparative Analysis of Machine Learning Approaches in Personality Prediction Using MBTI.- Performance Analysis of OFDM System on Multipath Fading and Inter Symbol Interference (ISI) Using AWGN.- Object Detection and Classification from a Real Time Video using SSD and YOLO Models.- A skin cancer image detection interface tool using VLF support vector machine classification.- Convolutional Neural Networks based Sentence Level Classification of Crime Documents.- An optimum fuzzy EOQ model for deteriorating items with shortages in a particular period.- Bangla Spoken numerals recognition by using HMM.
£179.99
Springer Verlag, Singapore Image and Graphics Technologies and Applications:
Book SynopsisThis book constitutes the refereed proceedings of the 16th Conference on Image and Graphics Technologies and Applications, IGTA 2021, held in Beijing, China in June, 2021. The 21 papers presented were carefully reviewed and selected from 86 submissions. They provide a forum for sharing progresses in the areas of image processing technology; image analysis and understanding; computer vision and pattern recognition; big data mining, computer graphics and VR, as well as image technology applications. The volume contains the following thematic blocks: image processing and enhancement techniques (image information acquisition, image/video coding, image/video transmission, image/video storage, compression, completion, dehazing, reconstruction and display, etc.); biometric identification techniques (biometric identification and authentication techniques including face, fingerprint, iris and palm-print, etc.); machine vision and 3D reconstruction (visual information acquisition, camera calibration, stereo vision, 3D reconstruction, and applications of machine vision in industrial inspection, etc.); image/video big data analysis and understanding (object detection and recognition, image/video retrieval, image segmentation, matching, analysis and understanding); computer graphics (modeling, rendering, algorithm simplification and acceleration techniques, realistic scene generation, 3D reconstruction algorithm, system and application, etc.); virtual reality and human-computer interaction (virtual scene generation techniques, tracing and positioning techniques for large-scale space, augmented reality techniques, human-computer interaction techniques based on computer vision, etc.); applications of image and graphics (image/video processing and transmission, biomedical engineering applications, information security, digital watermarking, text processing and transmission, remote sensing, telemetering, etc.); other research works and surveys related to the applications of image and graphics technology.Table of ContentsImage processing and enhancement techniques.- Biometric identification techniques.- Machine vision and 3D reconstruction.- Image/Video big data analysis and understanding.- Computer graphics.- Virtual reality and human-computer interaction.- Applications of image and graphics.
£58.49
Springer Face Recognition Across the Imaging Spectrum
Book SynopsisChapter 1. Face Profile Biometric Systems: An Overview.- Chapter 2. Using Facial Attractiveness as a Soft Biometric Trait to Enhance Face Recognition Performance.- Chapter 3. An Examination and Comparison of Fairness of Face and Ocular Recognition Across Gender at NIR Spectrum.- Chapter 4. Assessment of Face Recognition Algorithms over Periocular and Binocular Areas of the Human Face to Check Authentication Accuracy in the Event of Occlusion.- Chapter 5. The Effectiveness of Quality Training on Facial Examination and Identification.- Chapter 6. AdvBiom: Adversarial Attacks on Biometric Matchers.- Chapter 7. Heterogeneous Face Recognition with Prepended Domain Transformers.- Chapter 8. Demographic Fairness and Accountability of Audio and Video based Unimodal and Bimodal Deepfake Detectors.- Chapter 9. On Assessing the Impact of Ocular Pathologies on the Performance of Deep Learning Ocular Based Recognition Systems in the Visible and NIR Bands.- Chapter 10. Distance-Based Classification of Biometric Images: Leveraging Deep Learning Models.- Chapter 11. BiFaceGAN: Bimodal Face Image Synthesis.- Chapter 12. On Designing a Head Pose Estimation Approach in the Thermal Band through GAN-based Image Synthesis.- Chapter 13. On Designing an Optimal Configuration and calibration of Computer Monitors for Facial Image Analysis in Forensic Examination.
£143.99
Springer Verlag, Singapore Neural Information Processing: 29th International
Book SynopsisThe four-volume set CCIS 1791, 1792, 1793 and 1794 constitutes the refereed proceedings of the 29th International Conference on Neural Information Processing, ICONIP 2022, held as a virtual event, November 22–26, 2022. The 213 papers presented in the proceedings set were carefully reviewed and selected from 810 submissions. They were organized in topical sections as follows: Theory and Algorithms; Cognitive Neurosciences; Human Centered Computing; and Applications.The ICONIP conference aims to provide a leading international forum for researchers, scientists, and industry professionals who are working in neuroscience, neural networks, deep learning, and related fields to share their new ideas, progress, and achievements.Table of ContentsTheory and Algorithms.- Knowledge Transfer from Situation Evaluation to Multi-agent Reinforcement Learning.- Sequential three-way rules class-overlap under-sampling based on fuzzy hierarchical subspace for imbalanced data.- Two-stage Multilayer Perceptron Hawkes Process.- The Context Hierarchical Contrastive Learning for Time Series in Frequency Domain.- Hawkes Process via Graph Contrastive Discriminant representation Learning and Transformer capturing long-term dependencies.- A Temporal Consistency Enhancement Algorithm Based On Pixel Flicker Correction.- Data representation and clustering with double low-rank constraints.- RoMA: a Method for Neural Network Robustness Measurement and Assessment.- Independent Relationship Detection for Real-Time Scene Graph Generation.- A multi-label feature selection method based on feature graph with ridge regression and eigenvector centrality.- O3GPT: A Guidance-Oriented Periodic Testing Framework with Online Learning, Online Testing, and Online Feedback.- AFFSRN: Attention-Based Feature Fusion Super-Resolution Network.- Temporal-Sequential Learning with Columnar-Structured Spiking Neural Networks.- Graph Attention Transformer Network for Robust Visual Tracking.- GCL-KGE:Graph Contrastive Learning for Knowledge Graph Embedding.- Towards a Unified Benchmark for Reinforcement Learning in Sparse Reward Environments.- Effect of Logistic Activation Function and Multiplicative Input Noise on DNN-kWTA model.- A High-Speed SSVEP-Based Speller Using Continuous Spelling Method.- AAT: Non-Local Networks for Sim-to-Real Adversarial Augmentation Transfer.- Aggregating Intra-class and Inter-class information for Multi-label Text Classification.- Fast estimation of multidimensional regression functions by the Parzen kernel-based method.- ReGAE: Graph autoencoder based on recursive neural networks.- Efficient Uncertainty Quantification for Under-constraint Prediction following Learning using MCMC.- SMART: A Robustness Evaluation Framework for Neural Networks.- Time-aware Quaternion Convolutional Network for Temporal Knowledge Graph Reasoning.- SumBART - An improved BART model for abstractive text summarization.- Saliency-Guided Learned Image Compression for Object Detection.- Multi-Label Learning with Data Self-Augmentation.- MnRec: A News Recommendation Fusion Model Combining Multi-granularity Information.- Infinite Label Selection Method for Mutil-label Classification.- Simultaneous Perturbation Method for Multi-Task Weight Optimization in One-Shot Meta-Learning.- Searching for Textual Adversarial Examples with Learned Strategy.- Multivariate Time Series Retrieval with Binary Coding from Transformer. -Learning TSP Combinatorial Search and Optimization with Heuristic Search.- A Joint Learning Model for Open Set Recognition with Post-processing.- Cross-Layer Fusion for Feature Distillation.- MCHPT: A Weakly Supervise Based Merchant Pre-trained Model.- Progressive Latent Replay for efficient Generative Rehearsal.- Generalization Bounds for Set-to-Set Matching with Negative Sampling.- ADA: An Attention-Based Data Augmentation Approach to Handle Imbalanced Textual Datasets.- Countering the Anti-detection Adversarial Attacks.- Evolving Temporal Knowledge Graphs by Iterative Spatio-Temporal Walks.- Improving Knowledge Graph Embedding Using Dynamic Aggregation of Neighbor Information.- Generative Generalized Zero-Shot Learning based on Auxiliary-Features.- Learning Stable Representations with Progressive Autoencoder (PAE).- Effect of Image Down-sampling on Detection of Adversarial Examples .- Boosting the Robustness of Neural Networks with M-PGD.- StatMix: Data augmentation method that relies on image statistics in federated learning.- Classification by Components Including Chow's Reject Option. -Community discovery algorithm based on improved deep sparse autoencoder.- Fairly Constricted Multi-Objective Particle Swarm Optimization.- Argument Classification with BERT plus Contextual, Structural and Syntactic Features as Text.- Variance Reduction for Deep Q-Learning using Stochastic Recursive Gradient.- Optimizing Knowledge Distillation Via Shallow Texture Knowledge Transfer.- Unsupervised Domain Adaptation Supplemented with Generated Images.- MAR2MIX: A Novel Model for Dynamic Problem in Multi-Agent Reinforcement Learning.- Adversarial Training with Knowledge Distillation Considering Intermediate Representations in CNNs.- Deep Contrastive Multi-view Subspace Clustering.
£85.49
Springer Verlag, Singapore Neural Information Processing: 30th International
Book SynopsisThe six-volume set LNCS 14447 until 14452 constitutes the refereed proceedings of the 30th International Conference on Neural Information Processing, ICONIP 2023, held in Changsha, China, in November 2023. The 652 papers presented in the proceedings set were carefully reviewed and selected from 1274 submissions. They focus on theory and algorithms, cognitive neurosciences; human centred computing; applications in neuroscience, neural networks, deep learning, and related fields. Table of ContentsTheory and Algorithms.- Efficient Lightweight Network with Transformer-based Distillation for Micro-crack Detection of Solar Cells.- {MTLAN: Multi-Task Learning and Auxiliary Network for Enhanced Sentence Embedding.- Correlated Online k-Nearest Neighbors Regressor Chain for Online Multi-Output Regression.- Evolutionary Computation for Berth Allocation Problems: A Survey.- Cognitive Neurosciences.- Privacy-Preserving Travel Time Prediction for Internet of Vehicles: A Crowdsensing and Federated Learning Approach.- A Fine-Grained Domain Adaptation Method for Cross-Session Vigilance Estimation in SSVEP-Based BCI.- RMPE:Reducing Residual Membrane Potential Error for Enabling High-accuracy and Ultra-low-latency Spiking Neural Networks.- An improved target searching and imaging method for CSAR.- Block-Matching Multi-Pedestrian Tracking.- RPF3D: Range-Pillar Feature Deep Fusion 3D Detector for Autonomous Driving.- Traffic Signal Control Optimization Based on Deep Reinforcement Learning With Attention Mechanisms.- CMCI: A Robust Multimodal Fusion Method For Spiking Neural Networks.- A Weakly Supervised Deep Learning Model for Alzheimer's Disease Prognosis Using MRI and Incomplete Labels.- Two-Stream Spectral-Temporal Denoising Network for End-to-end Robust EEG-based Emotion Recognition.- Brain-inspired Binaural Sound Source Localization Method Based On Liquid State Machine.- A Causality-Based Interpretable Cognitive Diagnosis Model.- RoBrain: Towards Robust Brain-to-Image Reconstruction via Cross-Domain Contrastive Learning.- High-dimensional multi-objective PSO based on radial projection.- Link Prediction Based on the Sub-graphs Learning with Fused Features.- Naturalistic Emotion Recognition Using EEG and Eye Movements.- Task Scheduling With Improved Particle Swarm Optimization In Cloud Data Center.- Traffic Signal Optimization at T-shaped intersections Based on Deep Q Networks.- A Multi-task Framework for Solving Multimodal Multiobjective Optimization Problems.- Domain Generalized Object Detection with Triple Graph Reasoning Network.- RPUC: Semi-supervised 3D Biomedical Image Segmentation through Rectified Pyramid Unsupervised Consistency.- Cancellable iris recognition scheme based on inversion fusion and local ranking.- EWMIGCN: Emotional Weighting based Multimodal Interaction Graph Convolutional Networks for Personalized Prediction.- Neighborhood Learning for Artificial Bee Colony Algorithm: A Mini-survey.- Human Centred Computing.- Channel Attention Separable Convolution Network for Skin Lesion Segmentation.- A DNN-based Learning Framework for Continuous Movements Segmentation.- Neural-Symbolic Recommendation with Graph-Enhanced Information.- Contrastive Hierarchical Gating Networks for Rating Prediction.- Interactive Selection Recommendation Based on the Multi-Head Attention Graph Neural Network.- CM-TCN: Channel-aware Multi-scale Temporal Convolutional Networks For Speech Emotion Recognition.- FLDNet: A Foreground-Aware Network for Polyp Segmentation Leveraging Long-Distance Dependencies.- Domain-Invariant Task Optimization for Cross-domain Recommendation.- Ensemble of randomized neural network and boosted trees for eye tracking-based driver situation awareness recognition and interpretation.- Temporal Modeling Approach for Video Action Recognition Based on Vision-Language Models.- A Deep Learning Framework with Pruning RoI Proposal for Dental Caries Detection in Panoramic X-ray Images.- User stance aware network for rumor detection using semantic relation inference and temporal graph convolution.- IEEG-CT: A CNN and Transformer Based Method for Intracranial EEG Signal Classification.- Multi-Task Learning Network for Automatic Pancreatic Tumor Segmentation and Classification with Inter-Network Channel Feature Fusion.- Fast and Efficient Brain Extraction with Recursive MLP based 3D UNet.- A Hip-Knee Joint Coordination Evaluation System in Hemiplegic Individuals Based on Cyclogram Analysis.- Evaluation of football players' performance based on Multi-Criteria Decision Analysis approach and sensitivity analysis.
£75.99
Springer Verlag, Singapore Neural Information Processing: 30th International
Book SynopsisThe six-volume set LNCS 14447 until 14452 constitutes the refereed proceedings of the 30th International Conference on Neural Information Processing, ICONIP 2023, held in Changsha, China, in November 2023. The 652 papers presented in the proceedings set were carefully reviewed and selected from 1274 submissions. They focus on theory and algorithms, cognitive neurosciences; human centred computing; applications in neuroscience, neural networks, deep learning, and related fields. Table of ContentsText to Image Generation with Conformer-GAN.- MGFNet: A Multi-Granularity Feature Fusion and Mining Network for Visible-Infrared Person Re-Identification.- Isomorphic Dual-Branch Network for Non-homogeneous Image Dehazing and Super-Resolution.- Hi-Stega : A Hierarchical Linguistic Steganography Framework Combining Retrieval and Generation.- Effi-Seg: Rethinking EfficientNet Architecture for Real-time Semantic Segmentation.- Quantum Autoencoder Frameworks for Network Anomaly Detection.- Spatially-Aware Human-Object Interaction Detection with Cross-Modal Enhancement.- Intelligent trajectory tracking control of unmanned parafoil system based on SAC optimized LADRC.- CATS: Connection-aware and Interaction-based Text Steganalysis in Social Networks.- Syntax Tree Constrained Graph Network for Visual Question Answering.- CKR-Calibrator: Convolution Kernel Robustness Evaluation and Calibration.- SGLP-Net: Sparse Graph Label Propagation Network for Weakly-Supervised Temporal Action Localization.- VFIQ: A Novel Model of ViT-FSIMc Hybrid Siamese Network for Image Quality Assessment.- Spiking Reinforcement Learning for Weakly-supervised Anomaly Detection.- Resource-aware DNN Partitioning for Privacy-sensitive Edge-Cloud Systems.- A frequency reconfigurable multi-mode printed antenna.- Multi-view Contrastive learning for Knowledge-aware Recommendation.- PYGC: a PinYin Language Model Guided Correction Model for Chinese Spell Checking.- Empirical Analysis of Multi-label Classification on GitterCom using BERT.- A lightweight safety helmet detection network based on bidirectional connection module and Polarized Self-Attention.- Direct Inter-Intra View Association for Light Field Super-Resolution.- Responsive CPG-Based Locomotion Control for Quadruped Robots.- Vessel Behavior Anomaly Detection using Graph Attention Network.- TASFormer: Task-aware Image Segmentation Transformer.- Unsupervised Joint-Semantics Autoencoder Hashing for Multimedia Retrieval.- TKGR-RHETNE：A New Temporal Knowledge Graph Reasoning Model via Jointly Modeling Relevant Historical Event and Temporal Neighborhood Event Context.- High-Resolution Self-Attention with Fair Loss for Point Cloud Segmentation.- Transformer-based Video Deinterlacing Method.- SCME: A Self-Contrastive Method for Data-free and Query-Limited Model Extraction Attack.- CSEC: A Chinese Semantic Error Correction Dataset for Written Correction.- Contrastive Kernel Subspace Clustering.- UATR: An Uncertainty Aware Two-stage Refinement Model for Targeted Sentiment Analysis.- AttIN: Paying More Attention to Neighborhood Information for Entity Typing in Knowledge Graphs.- Text-based Person Re-ID by Saliency Mask and Dynamic Label Smoothing.- Robust Multi-view Spectral Clustering with Auto-encoder for Preserving Information.- Learnable Color Image Zero-Watermarking Based on Feature Comparison.- P-IoU: Accurate Motion Prediction based Data Association for Multi-Object Tracking.- WCA-VFnet:a dedicated complex forest smoke fire detector.- Label Selection Algorithm Based on Ant Colony Optimization and Reinforcement Learning for Multi-label Classification.- Reversible Data Hiding Based on Adaptive Embedding with Local Complexity.- Generalized Category Discovery with Clustering Assignment Consistency.- CInvISP: Conditional Invertible Image Signal Processing Pipeline.- Ignored Details in Eyes: Exposing GAN-generated Faces by Sclera.- A Developer Recommendation Method Based on Disentangled.- Graph Convolutional Network.- Novel Method for Radar Echo Target Detection.
£66.49
Springer Verlag, Singapore Biometric Recognition: 17th Chinese Conference,
Book SynopsisThis book constitutes the proceedings of the 17th Chinese Conference, CCBR 2023, held in Xuzhou, China, during December 1–3, 2023. The 41 full papers included in this volume were carefully reviewed and selected from 79 submissions. The volume is divided in topical sections named: Fingerprint, Palmprint and Vein Recognition; Face Detection, Recognition and Tracking; Affective Computing and Human-Computer Interface; Trustworthy, Privacy and Personal Data Security; Medical and Other Applications. Table of ContentsFingerprint, Palmprint and Vein Recognition.- Face Detection, Recognition and Tracking.- Affective Computing and Human-Computer Interface.- Gait, Iris and Other Biometrics.- Trustyworth, Privacy and Persondal Data Security.- Medical and Other Applications.
£61.74
Taylor & Francis Ltd InternetScale Pattern Recognition New Techniques for Voluminous Data Sets and Data Clouds
a huge range and FREE tracked UK delivery on ALL orders.
£56.99
Taylor & Francis Ltd Pattern Discovery in Bioinformatics
a huge range and FREE tracked UK delivery on ALL orders.
£56.99
Taylor & Francis Ltd Swarm Intelligence for Iris Recognition
a huge range and FREE tracked UK delivery on ALL orders.
£47.49
Taylor & Francis Ltd Swarm Intelligence for Iris Recognition
a huge range and FREE tracked UK delivery on ALL orders.
£25.38
Cambridge University Press Flexible Pattern Matching in Strings
Book SynopsisPresents recently developed algorithms for searching for simple, multiple and extended strings, regular expressions, exact and approximate matches.Trade Review'If you need efficient pattern matching for any kind of string then this is the only book I know that comes even close to providing you [with] the tools for the job.' The Journal of the ACCU'I really enjoyed reading and studying this book. I am convinced it is a must-read, especially chapters 4 through 6, for anyone who is involved in the task of designing algorithms for modern string or sequence matching.' Computing ReviewsTable of Contents1. Introduction; 2. String matching; 3. Multiple string matching; 4. Extended string matching; 5. Regular expression matching; 6. Approximate matching; 7. Conclusion; Bibliography; Index.
£51.29
Cambridge University Press Optical Pattern Recognition
a huge range and FREE tracked UK delivery on ALL orders.
£50.95
Cambridge University Press Multibiometrics for Human Identification
a huge range and FREE tracked UK delivery on ALL orders.
£117.19
Cambridge University Press Correlation Pattern Recognition
a huge range and FREE tracked UK delivery on ALL orders.
£49.39
Cambridge University Press Density Ratio Estimation in Machine Learning
a huge range and FREE tracked UK delivery on ALL orders.
£117.80
Cambridge University Press Scaling Up Machine Learning
a huge range and FREE tracked UK delivery on ALL orders.
£84.54
Cambridge University Press Planning English Sentences
a huge range and FREE tracked UK delivery on ALL orders.
£34.19
Cambridge University Press Correlation Pattern Recognition
a huge range and FREE tracked UK delivery on ALL orders.
£133.00
Cambridge University Press An Introduction to Support Vector Machines and Other Kernelbased Learning Methods
a huge range and FREE tracked UK delivery on ALL orders.
£75.99
Cambridge University Press Kernel Methods for Pattern Analysis
Book SynopsisThe kernel functions methodology described here provides a powerful and unified framework for disciplines ranging from neural networks and pattern recognition to machine learning and data mining. This book provides practitioners with a large toolkit of algorithms, kernels and solutions ready to be implemented, suitable for standard pattern discovery problems.Trade Review'Kernel methods form an important aspect of modern pattern analysis, and this book gives a lively and timely account of such methods. … if you want to get a good idea of the current research in this field, this book cannot be ignored.' SIAM Review'… the book provides an excellent overview of this growing field. I highly recommend it to those who are interested in pattern analysis and machine learning, and especailly to those who want to apply kernel-based methods to text analysis and bioinformatics problems.' Computing Reviews' … I enjoyed reading this book and am happy about is addition to my library as it is a valuable practitioner's reference. I especially liked the presentation of kernel-based pattern analysis algorithms in terse mathematical steps clearly identifying input data, output data, and steps of the process. The accompanying Matlab code or pseudocode is al extremely useful.' IAPR NewsletterTable of ContentsPreface; Part I. Basic Concepts: 1. Pattern analysis; 2. Kernel methods: an overview; 3. Properties of kernels; 4. Detecting stable patterns; Part II. Pattern Analysis Algorithms: 5. Elementary algorithms in feature space; 6. Pattern analysis using eigen-decompositions; 7. Pattern analysis using convex optimisation; 8. Ranking, clustering and data visualisation; Part III. Constructing Kernels: 9. Basic kernels and kernel types; 10. Kernels for text; 11. Kernels for structured data: strings, trees, etc.; 12. Kernels from generative models; Appendix A: proofs omitted from the main text; Appendix B: notational conventions; Appendix C: list of pattern analysis methods; Appendix D: list of kernels; References; Index.
£82.64
Cambridge University Press Applied Combinatorics on Words 105 Encyclopedia of Mathematics and its Applications Series Number 105
a huge range and FREE tracked UK delivery on ALL orders.
£151.05
Cambridge University Press Mathematical Analysis of Machine Learning
Book SynopsisThis self-contained textbook introduces students and researchers of AI to the key mathematical concepts and techniques necessary to learn and analyze machine learning algorithms. Readers will gain the technical knowledge needed to understand research papers in theoretical machine learning, without much difficulty.Trade Review'This graduate-level text gives a thorough, rigorous and up-to-date treatment of the main mathematical tools that have been developed for the analysis and design of machine learning methods. It is ideal for a graduate class, and the exercises at the end of each chapter make it suitable for self-study. An excellent addition to the literature from one of the leading researchers in this area, it is sure to become a classic.' Peter Bartlett, University of California, Berkeley'This book showcases the breadth and depth of mathematical ideas in learning theory. The author has masterfully synthesized techniques from the many disciplines that have contributed to this subject, and presented them in an accessible format that will be appreciated by both newcomers and experts alike. Readers will learn the tools-of-the-trade needed to make sense of the research literature and to express new ideas with clarity and precision.' Daniel Hsu, Columbia University'Tong Zhang shares in this book his deep and broad knowledge of machine learning, writing an impressively comprehensive and up-to-date reference text, providing a rigorous and rather advanced treatment of the most important topics and approaches in the mathematical study of machine learning. As an authoritative reference and introduction, his book will be a great asset to the field.' Robert Schapire, Microsoft Research'This book gives a systematic treatment of the modern mathematical techniques that are commonly used in the design and analysis of machine learning algorithms. Written by a key contributor to the field, it is a unique resource for graduate students and researchers seeking to gain a deep understanding of the theory of machine learning.' Shai Shalev-Shwartz, Hebrew University of JerusalemTable of Contents1. Introduction; 2. Basic probability inequalities for sums of independent random variables; 3. Uniform convergence and generalization analysis; 4. Empirical covering number analysis and symmetrization; 5. Covering number estimates; 6. Rademacher complexity and concentration inequalities; 7. Algorithmic stability analysis; 8. Model selection; 9. Analysis of kernel methods; 10. Additive and sparse models; 11. Analysis of neural networks; 12. Lower bounds and minimax analysis; 13. Probability inequalities for sequential random variables; 14. Basic concepts of online learning; 15. Online aggregation and second order algorithms; 16. Multi-armed bandits; 17. Contextual bandits; 18. Reinforcement learning; A. Basics of convex analysis; B. f-Divergence of probability measures; References; Author index; Subject index.
£42.74
Cambridge University Press The Cambridge Handbook of Cognitive Linguistics
Book SynopsisThe best survey of cognitive linguistics available, this Handbook provides a thorough explanation of its rich methodology, key results, and interdisciplinary context. With in-depth coverage of the research questions, basic concepts, and various theoretical approaches, the Handbook addresses newly emerging subfields and shows their contribution to the discipline. The Handbook introduces fields of study that have become central to cognitive linguistics, such as conceptual mappings and construction grammar. It explains all the main areas of linguistic analysis traditionally expected in a full linguistics framework, and includes fields of study such as language acquisition, sociolinguistics, diachronic studies, and corpus linguistics. Setting linguistic facts within the context of many other disciplines, the Handbook will be welcomed by researchers and students in a broad range of disciplines, including linguistics, cognitive science, neuroscience, gesture studies, computational linguisticTrade ReviewAdvance praise: 'This is the definitive introduction to cognitive linguistics that the mature field deserves, written by the leading practitioners in cognitive approaches to grammar, semantics, conceptual structure, phonology, and everything in-between (and all around). I can't imagine a better introduction for students of language.' Benjamin K. Bergen, University of California, San DiegoTable of ContentsIntroduction Barbara Dancygier; Part I. Language in Cognition and Culture: 1. Opening commentary: language in cognition and culture N. J. Enfield; 2. Relationships between language and cognition Daniel Casasanto; 3. The study of indigenous languages Sally Rice; 4. First language acquisition Laura E. De Ruiter and Anna L. Theakston; 5. Second language acquisition Andrea Tyler; Part II. Language, Body, and Multimodal Communication: 6. Opening commentary: polytropos and communication in the wild Mark Turner; 7. Signed languages Sherman Wilcox and Corinne Occhino; 8. Gesture, language, and cognition Kensy Cooperrider and Susan Goldin-Meadow; 9. Multimodality in interaction Kurt Feyaerts, Geert Brône and Bert Oben; 10. Viewpoint Lieven Vandelanotte; 11. Embodied intersubjectivity Jordan Zlatev; 12. Intersubjectivity and grammar Ronny Boogaart and Alex Reuneker; Part III. Aspects of Linguistic Analysis: 13. Opening commentary: linguistic analysis John Newman; 14. Phonology Geoffrey S. Nathan; 15. The construction of words Geert Booij; 16. Lexical semantics John R. Taylor; 17. Cognitive grammar Ronald W. Langacker; 18. From constructions to construction grammars Thomas Hoffmann; 19. Construction grammars Thomas Hoffmann; 20. Cognitive linguistics and pragmatics Kerstin Fischer; 21. Fictive interaction Esther Pascual and Todd Oakley; 22. Diachronic approaches Alexander Bergs; Part IV. Conceptual Mappings: 23. Opening commentary: conceptual mappings Eve Sweetser; 24. Conceptual metaphor Karen Sullivan; 25. Metonymy Jeannette Littlemore; 26. Conceptual blending theory Todd Oakley and Esther Pascual; 27. Embodiment Raymond W. Gibbs, Jr; 28. Corpus linguistics and metaphor Elena Semino; 29. Metaphor, simulation, and fictive motion Teenie Matlock; Part V. Methodological Approaches: 30. Opening commentary: getting the measure of meaning Chris Sinha; 31. The quantitative turn Laura A. Janda; 32. Language and the brain Seana Coulson; 33. Cognitive sociolinguistics Willem B. Hollmann; 34. Computational resources: framenet and constructicon Hans C. Boas; 35. Computational approaches to metaphor: the case of MetaNet Oana A. David; 36. Corpus approaches Stefan Gries; 37. Cognitive linguistics and the study of textual meaning Barbara Dancygier; Part VI. Concepts and Approaches: Space and Time: 38. Linguistic patterns of space and time vocabulary Eve Sweetser and Alice Gaby; 39. Space-time mappings beyond language Alice Gaby and Eve Sweetser; 40. Conceptualizing time in terms of space: experimental evidence Tom Gijssels and Daniel Casasanto; 41. Discovering spatiotemporal concepts in discourse Thora Tenbrink.
£47.99
Cambridge University Press Introduction to Graph Signal Processing
Book SynopsisAn intuitive and accessible text explaining the fundamentals and applications of graph signal processing. Requiring only an elementary understanding of linear algebra, it covers both basic and advanced topics, including node domain processing, graph signal frequency, sampling, and graph signal representations, as well as how to choose a graph. Understand the basic insights behind key concepts and learn how graphs can be associated to a range of specific applications across physical, biological and social networks, distributed sensor networks, image and video processing, and machine learning. With numerous exercises and Matlab examples to help put knowledge into practice, and a solutions manual available online for instructors, this unique text is essential reading for graduate and senior undergraduate students taking courses on graph signal processing, signal processing, information processing, and data analysis, as well as researchers and industry professionals.Table of Contents1. Introduction; 2. Node domain processing; 3. Graph signal frequency-Spectral graph theory; 4. Sampling; 5. Graph signal representations; 6. How to choose a graph; 7. Applications; Appendix A. Linear algebra and signal representations; Appendix B. GSP with Matlab: the GraSP toolbox; References; Index.
£66.49
Cambridge University Press Scaling Up Machine Learning
Book SynopsisIn many practical situations it is impossible to run existing machine learning methods on a single computer, because either the data is too large or the speed and throughput requirements are too demanding. Researchers and practitioners will find here a variety of machine learning methods developed specifically for parallel or distributed systems, covering algorithms, platforms and applications.Trade Review'One of the landmark achievements of our time is the ability to extract value from large volumes of data. Engineering and algorithmic developments on this front have gelled substantially in recent years, and are quickly being reduced to practice in widely available, reusable forms. This book provides a broad and timely snapshot of the state of developments in scalable machine learning, which should be of interest to anyone who wishes to understand and extend the state of the art in analyzing data.' Joseph M. Hellerstein, University of California, Berkeley'This is a book that every machine learning practitioner should keep in their library.' Yoram Singer, Google Inc.'The contributions in this book run the gamut from frameworks for large-scale learning to parallel algorithms to applications, and contributors include many of the top people in this burgeoning subfield. Overall this book is an invaluable resource for anyone interested in the problem of learning from and working with big datasets.' William W. Cohen, Carnegie Mellon University, Pennsylvania'This unique, timely book provides a 360 degrees view and understanding of both conceptual and practical issues that arise when implementing leading machine learning algorithms on a wide range of parallel and high-performance computing platforms. It will serve as an indispensable handbook for the practitioner of large-scale data analytics and a guide to dealing with BIG data and making sound choices for efficient applying learning algorithms to them. It can also serve as the basis for an attractive graduate course on parallel/distributed machine learning and data mining.' Joydeep Ghosh, University of TexasTable of Contents1. Scaling up machine learning: introduction Ron Bekkerman, Mikhail Bilenko and John Langford; Part I. Frameworks for Scaling Up Machine Learning: 2. Mapreduce and its application to massively parallel learning of decision tree ensembles Biswanath Panda, Joshua S. Herbach, Sugato Basu and Roberto J. Bayardo; 3. Large-scale machine learning using DryadLINQ Mihai Budiu, Dennis Fetterly, Michael Isard, Frank McSherry and Yuan Yu; 4. IBM parallel machine learning toolbox Edwin Pednault, Elad Yom-Tov and Amol Ghoting; 5. Uniformly fine-grained data parallel computing for machine learning algorithms Meichun Hsu, Ren Wu and Bin Zhang; Part II. Supervised and Unsupervised Learning Algorithms: 6. PSVM: parallel support vector machines with incomplete Cholesky Factorization Edward Chang, Hongjie Bai, Kaihua Zhu, Hao Wang, Jian Li and Zhihuan Qiu; 7. Massive SVM parallelization using hardware accelerators Igor Durdanovic, Eric Cosatto, Hans Peter Graf, Srihari Cadambi, Venkata Jakkula, Srimat Chakradhar and Abhinandan Majumdar; 8. Large-scale learning to rank using boosted decision trees Krysta M. Svore and Christopher J. C. Burges; 9. The transform regression algorithm Ramesh Natarajan and Edwin Pednault; 10. Parallel belief propagation in factor graphs Joseph Gonzalez, Yucheng Low and Carlos Guestrin; 11. Distributed Gibbs sampling for latent variable models Arthur Asuncion, Padhraic Smyth, Max Welling, David Newman, Ian Porteous and Scott Triglia; 12. Large-scale spectral clustering with Mapreduce and MPI Wen-Yen Chen, Yangqiu Song, Hongjie Bai, Chih-Jen Lin and Edward Y. Chang; 13. Parallelizing information-theoretic clustering methods Ron Bekkerman and Martin Scholz; Part III. Alternative Learning Settings: 14. Parallel online learning Daniel Hsu, Nikos Karampatziakis, John Langford and Alex J. Smola; 15. Parallel graph-based semi-supervised learning Jeff Bilmes and Amarnag Subramanya; 16. Distributed transfer learning via cooperative matrix factorization Evan Xiang, Nathan Liu and Qiang Yang; 17. Parallel large-scale feature selection Jeremy Kubica, Sameer Singh and Daria Sorokina; Part IV. Applications: 18. Large-scale learning for vision with GPUS Adam Coates, Rajat Raina and Andrew Y. Ng; 19. Large-scale FPGA-based convolutional networks Clement Farabet, Yann LeCun, Koray Kavukcuoglu, Berin Martini, Polina Akselrod, Selcuk Talay and Eugenio Culurciello; 20. Mining tree structured data on multicore systems Shirish Tatikonda and Srinivasan Parthasarathy; 21. Scalable parallelization of automatic speech recognition Jike Chong, Ekaterina Gonina, Kisun You and Kurt Keutzer.
£42.74
PublicAffairs We See It All: Liberty and Justice in an Age of
Book Synopsis
£22.40
