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Empirical Inference

Festschrift in Honor of Vladimir N. Vapnik

  • Bernhard Schölkopf
  • Zhiyuan Luo
  • Vladimir Vovk

Table of contents

  1. Front Matter
    Pages i-xix
  2. History of Statistical Learning Theory

    1. Front Matter
      Pages 1-1
    2. Alexey Ya. Chervonenkis
      Pages 13-20
  3. Theory and Practice of Statistical Learning Theory

    1. Front Matter
      Pages 21-24
    2. Robert E. Schapire
      Pages 37-52
    3. Silvia Villa, Lorenzo Rosasco, Tomaso Poggio
      Pages 59-69
    4. Robert C. Williamson
      Pages 71-80
    5. Jason Weston
      Pages 81-93
    6. David McAllester, Takintayo Akinbiyi
      Pages 95-103
    7. Vladimir Vovk
      Pages 105-116
    8. Christian Widmer, Marius Kloft, Gunnar Rätsch
      Pages 117-127
    9. Bernhard Schölkopf, Dominik Janzing, Jonas Peters, Eleni Sgouritsa, Kun Zhang, Joris Mooij
      Pages 129-141
    10. Luc Devroye, Paola G. Ferrario, László Györfi, Harro Walk
      Pages 143-160
    11. Ran Gilad-Bachrach, Chris J. C. Burges
      Pages 161-175
    12. Nicolò Cesa-Bianchi, Ohad Shamir
      Pages 177-194
    13. Eric Gautier, Alexandre B. Tsybakov
      Pages 195-204
    14. Andreas Argyriou, Luca Baldassarre, Charles A. Micchelli, Massimiliano Pontil
      Pages 205-216
    15. Vladimir Koltchinskii
      Pages 217-230
    16. John C. Snyder, Sebastian Mika, Kieron Burke, Klaus-Robert Müller
      Pages 245-259
    17. Mark Stevens, Samy Bengio, Yoram Singer
      Pages 261-271
  4. Back Matter
    Pages 285-287

About this book

Introduction

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.

Keywords

Bayesian theory Kernels Learning Machine learning Optimization Statistical learning theory Support vector machines (SVMs) VC (Vapnik-Chervonenkis) dimension

Editors and affiliations

  • Bernhard Schölkopf
    • 1
  • Zhiyuan Luo
    • 2
  • Vladimir Vovk
    • 3
  1. 1.Max Planck Institute for Intelligent SystemsTübingenGermany
  2. 2.Dept. of Computer ScienceRoyal Holloway, University of LondonEghamUnited Kingdom
  3. 3.Department of Computer ScienceRoyal Holloway, University of LondonEgham, SurreyUnited Kingdom

Bibliographic information

  • DOI https://doi.org/10.1007/978-3-642-41136-6
  • Copyright Information Springer-Verlag Berlin Heidelberg 2013
  • Publisher Name Springer, Berlin, Heidelberg
  • eBook Packages Computer Science
  • Print ISBN 978-3-642-41135-9
  • Online ISBN 978-3-642-41136-6
  • Buy this book on publisher's site
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