首次提出SVM的英文论文，105页pdf.pdf

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Information Science and Statistics Series Editors: M. Jordan J. Kleinberg B. Schölkopf

Information Science and Statistics Akaike and Kitagawa: The Practice of Time Series Analysis. Cowell, Dawid, Lauritzen, and Spiegelhalter: Probabilistic Networks and Expert Systems. Doucet, de Freitas, and Gordon: Sequential Monte Carlo Methods in Practice. Fine: Feedforward Neural Network Methodology. Hawkins and Olwell: Cumulative Sum Charts and Charting for Quality Improvement. Jensen: Bayesian Networks and Decision Graphs. Marchette: Computer Intrusion Detection and Network Monitoring: A Statistical Viewpoint. Rubinstein and Kroese: The Cross-Entropy Method: A Unified Approach to Combinatorial Optimization, Monte Carlo Simulation, and Machine Learning. Studen´y: Probabilistic Conditional Independence Structures. Vapnik: The Nature of Statistical Learning Theory, Second Edition. Wallace: Statistical and Inductive Inference by Minimum Massage Length.

Vladimir Vapnik Estimation of Dependences Based on Empirical Data Reprint of 1982 Edition Empirical Inference Science Afterword of 2006

Vladimir Vapnik NEC Labs America 4 Independence Way Princeton, NJ 08540 vlad@nec-labs.com Samuel Kotz (Translator) Department of Engineering Management and Systems Engineering The George Washington University Washington, D.C. 20052 Series Editors: Michael Jordan Division of Computer Science and Department of Statistics University of California, Berkeley Berkeley, CA 94720 USA Jon Kleinberg Department of Computer Science Cornell University Ithaca, NY 14853 USA Bernhard Schölkopf Max Planck Institute for Biological Cybernetics Spemannstrasse 38 72076 Tübingen Germany Library of Congress Control Number: 2005938355 ISBN-10: 0-387-30865-2 ISBN-13: 978-0387-30865-4 Printed on acid-free paper. © 2006 Springer Science+Business Media, Inc. All rights reserved. This work may not be translated or copied in whole or in part without the written permission of the publisher (Springer Science+Business Media, Inc., 233 Spring Street, New York, NY 10013, USA), except for brief excerpts in connection with reviews or scholarly analysis. Use in connection with any form of information storage and retrieval, electronic adaptation, computer software, or by similar or dissimilar methodology now known or hereafter developed is forbidden. The use in this publication of trade names, trademarks, service marks, and similar terms, even if they are not identified as such, is not to be taken as an expression of opinion as to whether or not they are subject to proprietary rights. Printed in the United States of America. (MVY) 9 8 7 6 5 4 3 2 1 springer.com

Vladimir Vapnik Estimation of Dependences Based on Empirical Data Translated by Samuel Kotz With 22 illustrations

To the students of my students in memory of my violin teacher Ilia Shtein and PhD advisor Alexander Lerner, who taught me several important things that are very difﬁcult to learn from books.

PREFACE Twenty-ﬁve years have passed since the publication of the Russian version of the book Estimation of Dependencies Based on Empirical Data (EDBED for short). Twenty- ﬁve years is a long period of time. During these years many things have happened. Looking back, one can see how rapidly life and technology have changed, and how slow and difﬁcult it is to change the theoretical foundation of the technology and its philosophy. I pursued two goals writing this Afterword: to update the technical results presented in EDBED (the easy goal) and to describe a general picture of how the new ideas developed over these years (a much more difﬁcult goal). The picture which I would like to present is a very personal (and therefore very biased) account of the development of one particular branch of science, Empirical In- ference Science. Such accounts usually are not included in the content of technical publications. I have followed this rule in all of my previous books. But this time I would like to violate it for the following reasons. First of all, for me EDBED is the important milestone in the development of empirical inference theory and I would like to explain why. Sec- ond, during these years, there were a lot of discussions between supporters of the new paradigm (now it is called the VC theory1) and the old one (classical statistics). Being involved in these discussions from the very beginning I feel that it is my obligation to describe the main events. The story related to the book, which I would like to tell, is the story of how it is difﬁcult to overcome existing prejudices (both scientiﬁc and social), and how one should be careful when evaluating and interpreting new technical concepts. This story can be split into three parts that reﬂect three main ideas in the develop- ment of empirical inference science: from the pure technical (mathematical) elements of the theory to a new paradigm in the philosophy of generalization. 1VC theory is an abbreviation for Vapnik–Chervonenkis theory. This name for the corresponding theory appeared in the 1990s after EDBED was published. 405

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首次提出SVM的英文论文，105页pdf.pdf

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