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Empirical Inference - Festschrift in Honor of Vladimir N. Vapnik (Paperback, Softcover reprint of the original 1st ed. 2013)
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Empirical Inference - Festschrift in Honor of Vladimir N. Vapnik (Paperback, Softcover reprint of the original 1st ed. 2013)
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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, Leon 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.
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