Overview
- Honours one of the pioneers of machine learning
- Contributing authors are among the leading authorities in these domains
- Of interest to researchers and engineers in the fields of machine learning, statistics, and optimization
- Includes supplementary material: sn.pub/extras
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Table of contents (23 chapters)
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Front Matter
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History of Statistical Learning Theory
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Front Matter
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Theory and Practice of Statistical Learning Theory
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Front Matter
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Keywords
About this book
This book honours the outstanding contributions of Vladimir Vapnik, a rare example of a scientist for whom the following statements hold true simultaneously: his work led to the inception of a new field of research, the theory of statistical learning and empirical inference; he has lived to see the field blossom; and he is still as active as ever. He started analyzing learning algorithms in the 1960s and he invented the first version of the generalized portrait algorithm. He later developed one of the most successful methods in machine learning, the support vector machine (SVM) – more than just an algorithm, this was a new approach to learning problems, pioneering the use of functional analysis and convex optimization in machine learning.
Part I of this book contains three chapters describing and witnessing some of Vladimir Vapnik's contributions to science. In the first chapter, Léon Bottou discusses the seminal paper published in 1968 by Vapnik and Chervonenkis that lay the foundations of statistical learning theory, and the second chapter is an English-language translation of that original paper. In the third chapter, Alexey Chervonenkis presents a first-hand account of the early history of SVMs and valuable insights into the first steps in the development of the SVM in the framework of the generalised portrait method.
The remaining chapters, by leading scientists in domains such as statistics, theoretical computer science, and mathematics, address substantial topics in the theory and practice of statistical learning theory, including SVMs and other kernel-based methods, boosting, PAC-Bayesian theory, online and transductive learning, loss functions, learnable function classes, notions of complexity for function classes, multitask learning, and hypothesis selection.These contributions include historical and context notes, short surveys, and comments on future research directions.
This book will be of interest to researchers, engineers, and graduate students engaged with all aspects of statistical learning.
Editors and Affiliations
Bibliographic Information
Book Title: Empirical Inference
Book Subtitle: Festschrift in Honor of Vladimir N. Vapnik
Editors: Bernhard Schölkopf, Zhiyuan Luo, Vladimir Vovk
DOI: https://doi.org/10.1007/978-3-642-41136-6
Publisher: Springer Berlin, Heidelberg
eBook Packages: Computer Science, Computer Science (R0)
Copyright Information: Springer-Verlag Berlin Heidelberg 2013
Hardcover ISBN: 978-3-642-41135-9Published: 02 January 2014
Softcover ISBN: 978-3-662-52511-1Published: 23 August 2016
eBook ISBN: 978-3-642-41136-6Published: 11 December 2013
Edition Number: 1
Number of Pages: XIX, 287
Number of Illustrations: 7 b/w illustrations, 26 illustrations in colour
Topics: Artificial Intelligence, Statistical Theory and Methods, Probability and Statistics in Computer Science, Optimization