Search results for ""Author Claus Weihs""

This volume contains revised versions of selected papers presented during the 28th Annual Conference of the Gesellschaft fur .. Klassi?kation (GfKl), the German Classi?cation Society. The conference was held at the Universit. at Dortmund in Dortmund, Germany, in March 2004. Wolfgang Gaul chaired the program committee, Claus Weihs and Ernst-Erich Doberkat were the local organizers. Patrick Groenen, Iven van Mechelen, and their colleagues of the Vereniging voor Ordinatie en Classi?catie (VOC), the Dutch-Flemish Classi?cation Society, organized special VOC sessions. The programcommittee recruited17 notable and internationallyreno- ed invited speakers for plenary and semi-plenary talks on their current - search work regarding classi?cation and data analysis methods as well as - plications.Inaddition,172invitedandcontributedpapersbyauthorsfrom18 countrieswerepresentedatthe conferencein 52parallelsessionsrepresenting the whole ?eld addressed by the title of the conference "Classi?cation: The Ubiquitous Challenge". Among these 52 sessions the VOC organizedsessions on Mixture Modelling, Optimal Scaling, Multiway Methods, and Psychom- ricswith18papers.Overall,theconference,whichistraditionallydesignedas an interdisciplinary event, again provided an attractive forum for discussions and mutual exchange of knowledge. Besides the results obtained in the fundamental subjects Classi?cation and Data Analysis, the talks in the applied areas focused on various app- cation topics. Moreover, along with the conference a competition on "Social Milieus in Dortmund", co-organized by the city of Dortmund, took place. Hence the presentation of the papers in this volume is arranged in the f- lowing parts: I. (Semi-)Plenary Presentations II. Classi?cation and Data Analysis III. Applications, and IV. Contest: Social Milieus in Dortmund.

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A new and refreshingly different approach to presenting the foundations of statistical algorithms, Foundations of Statistical Algorithms: With References to R Packages reviews the historical development of basic algorithms to illuminate the evolution of today’s more powerful statistical algorithms. It emphasizes recurring themes in all statistical algorithms, including computation, assessment and verification, iteration, intuition, randomness, repetition and parallelization, and scalability. Unique in scope, the book reviews the upcoming challenge of scaling many of the established techniques to very large data sets and delves into systematic verification by demonstrating how to derive general classes of worst case inputs and emphasizing the importance of testing over a large number of different inputs. Broadly accessible, the book offers examples, exercises, and selected solutions in each chapter as well as access to a supplementary website. After working through the material covered in the book, readers should not only understand current algorithms but also gain a deeper understanding of how algorithms are constructed, how to evaluate new algorithms, which recurring principles are used to tackle some of the tough problems statistical programmers face, and how to take an idea for a new method and turn it into something practically useful.

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This book provides a comprehensive overview of music data analysis, from introductory material to advanced concepts. It covers various applications including transcription and segmentation as well as chord and harmony, instrument and tempo recognition. It also discusses the implementation aspects of music data analysis such as architecture, user interface and hardware. It is ideal for use in university classes with an interest in music data analysis. It also could be used in computer science and statistics as well as musicology.

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