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The Nature of Statistical Learning Theory
 
 
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The Nature of Statistical Learning Theory [Hardcover]

Vladimir Vapnik (Author)
4.3 out of 5 stars  See all reviews (3 customer reviews)
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Review

From the reviews of the second edition: ZENTRALBLATT MATH "...written in a concise style. It must be recommended to scientists of statistics, mathematics, physics, and computer science." SHORT BOOK REVIEWS "This interesting book helps a reader to understand the interconnections between various streams in the empirical modeling realm and may be recommended to any reader who feels lost in modern terminology, such as artificial intelligence, neural networks, machine learning etcetera." "The book by Vapnik focuses on how to estimate a function of parameters from empirical data … . The book is concisely written and is intended to be useful to statisticians, computer scientists, mathematicians, and physicists. … This book is very well written at a very high level of abstract thinking and comprehension. The references are up-to-date." (Ramalingam Shanmugam, Journal of Statistical Computation and Simulation, Vol. 75 (2), February, 2005) "The aim of the book is to introduce a wide range of readers to the fundamental ideas of statistical learning theory. … Each chapter is supplemented by ‘Reasoning and Comments’ which describe the relations between classical research in mathematical statistics and research in learning theory. … The book is well suited to promote the ideas of statistical learning theory and can be warmly recommended to all who are interested in computer learning problems." (S. Vogel, Metrika, June, 2002)

Book Description

The aim of this book is to discuss the fundamental ideas which lie behind the statistical theory of learning and generalization. It considers learning as a general problem of function estimation based on empirical data. Omitting proofs and technical details, the author concentrates on discussing the main results of learning theory and their connections to fundamental problems in statistics. This second edition contains three new chapters devoted to further development of the learning theory and SVM techniques. Written in a readable and concise style, the book is intended for statisticians, mathematicians, physicists, and computer scientists.
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First Sentence
More than thirty five years ago F. Rosenblatt suggested the first model of a learning machine, called the perceptron; this is when the mathematical analysis of learning processes truly began. Read the first page
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Front Cover | Copyright | Table of Contents | Excerpt | Index | Back Cover
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3 Reviews
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4.3 out of 5 stars (3 customer reviews)
 
 
 
 
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3.0 out of 5 stars worth reading, Sep 22 2001
This review is from: The Nature of Statistical Learning Theory (Hardcover)
A good, albeit highly idiosyncratic, guide to Statistical Learning. The highly personal account of the theory is both the strong point and the drawback of the treatise. On one side, Vapnick never loses sight of the big picture, and gives illuminating insights and formulations of the "basic problems" (as he calls them), that are not found in any other book. The lack of proofs and the slightly erratic organization of the topic make for a brisk, enjoyable reading. On the minus side, the choice of the topics is very biased. In this respect, the book is a self-congratulatory tribute by the author to himself: it appears that the foundations of statistical learning were single-handedly laid by him and his collaborators. This is not really the case. Consistency of the Empircal Risk Measure is rather trivial from the viewpoint of a personal trained in asymptotic statistics, and interval estimators for finite data sets are the subject of much advanced statistical literature. Finally, SVMs and neural nets are just a part of the story, and probably not the most interesting.
In a nutshell, what Vapnick shows, he shows very well, and is able to provide the "why" of things as no one else. What he doesn't show... you'll have to find somewhere else (the recent Book of Friedman Hastie & Tibs is an excellent starting point).
A last remark. The book is rich in grammatical errors and typos. They could have been corrected in the second edition, but do not detract from the book's readability.
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5.0 out of 5 stars A very nice book to get ideas on support vector machines, Aug 28 2000
This review is from: The Nature of Statistical Learning Theory (Hardcover)
This is a very readable book by an authority on this subject. The book starts with the statistical learning theory, pioneered by the author and co-worker's work, and gradually leads to the path of discovery of support vector machines. An excellent and distinctive property of support vector machines is that they are robust to small data perturbation and have good generalization ability with function complexity being controlled by VC dimension. The treatment of nonlinear kernel classification and regression is given for the first time in the first edition. The 2nd edition includes significant updates including a separate chapter on support vector regression as well as a section on logistic regression using the support vector approach. Most computations involved in this book can be implemented using a quadratic programming package. The connections of support vector machines to traditional statistical modeling such as kernel density and regression and model selection are also discussed. Thus, this book will be an excellent starting point for learning support vector machines.
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5.0 out of 5 stars A research field described by the man who invented it, Feb 25 2000
This review is from: The Nature of Statistical Learning Theory (Hardcover)
Vapnik and collaborators have developed the field of statistical learning theory underlying recent advances in machine learning and artificial intelligence (e.g. support vector machines). This book almost accomplishes the formidable task of comprehensibly describing the essential ideas of learning theory to non-statisticians. It contains ample theorems but almost no proofs.
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