Empirical Inference

Festschrift in Honor of Vladimir N. Vapnik

Editors: Schoelkopf, Bernhard, Luo, Zhiyuan, Vovk, Vladimir (Eds.)

  • 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
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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.

Table of contents (23 chapters)

  • In Hindsight: Doklady Akademii Nauk SSSR, 181(4), 1968

    Bottou, Léon

    Pages 3-5

  • On the Uniform Convergence of the Frequencies of Occurrence of Events to Their Probabilities

    Vapnik, Vladimir N. (et al.)

    Pages 7-12

  • Early History of Support Vector Machines

    Chervonenkis, Alexey Ya.

    Pages 13-20

  • Some Remarks on the Statistical Analysis of SVMs and Related Methods

    Steinwart, Ingo

    Pages 25-36

  • Explaining AdaBoost

    Schapire, Robert E.

    Pages 37-52

Buy this book

eBook $99.00
price for USA (gross)
  • ISBN 978-3-642-41136-6
  • Digitally watermarked, DRM-free
  • Included format: PDF, EPUB
  • ebooks can be used on all reading devices
  • Immediate eBook download after purchase
Hardcover $129.00
price for USA
  • ISBN 978-3-642-41135-9
  • Free shipping for individuals worldwide
  • Usually dispatched within 3 to 5 business days.
Softcover $129.00
price for USA
  • ISBN 978-3-662-52511-1
  • Free shipping for individuals worldwide
  • Usually dispatched within 3 to 5 business days.
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Bibliographic Information

Bibliographic Information
Book Title
Empirical Inference
Book Subtitle
Festschrift in Honor of Vladimir N. Vapnik
Editors
  • Bernhard Schoelkopf
  • Zhiyuan Luo
  • Vladimir Vovk
Copyright
2013
Publisher
Springer-Verlag Berlin Heidelberg
Copyright Holder
Springer-Verlag Berlin Heidelberg
eBook ISBN
978-3-642-41136-6
DOI
10.1007/978-3-642-41136-6
Hardcover ISBN
978-3-642-41135-9
Softcover ISBN
978-3-662-52511-1
Edition Number
1
Number of Pages
XIX, 287
Number of Illustrations and Tables
7 b/w illustrations, 26 illustrations in colour
Topics