Statistical learning theory

著者

• Vapnik, Vladimir Naumovich

書誌事項

Statistical learning theory

Vladimir N. Vapnik

（Adaptive and learning systems for signal processing, communications, and control）

John Wiley & Sons, c1998

• : cloth
• : hard

注記

"A Wiley-interscience publication."

Includes bibliographical references (p. 723-732) and index

内容説明・目次

内容説明

A comprehensive look at learning and generalization theory. The statistical theory of learning and generalization concerns the problem of choosing desired functions on the basis of empirical data. Highly applicable to a variety of computer science and robotics fields, this book offers lucid coverage of the theory as a whole. Presenting a method for determining the necessary and sufficient conditions for consistency of learning process, the author covers function estimates from small data pools, applying these estimations to real-life problems, and much more.

目次

Preface xxi Introduction: The Problem of induction and Statistical inference 1 I Theory of learning and generation 1 Two Approches to the learnig problem 19 Appendix to chapter 1: Methods for solving III-posed problems 51 2 Estimation of the probability Measure and problem of learning 59 3 Conditions for Consistency of Empirical Risk Minimization Principal 79 4 Bounds on the Risk for indicator Loss Functions 121 Appendix to Chapter 4: Lower Bounds on the Risk of the ERM Principle 169 5 Bounds on the Risk for Real-valued loss functions 183 6 The structural Risk Minimization Principle 219 Appendix to chapter 6: Estimating Functions on the basis of indirect measurements 271 7 stochastic III-posed problems 293 8 Estimating the values of Function at given points 339 II Support Vector Estimation of Functions 9 Perceptions and their Generalizations 375 10 The Support Vector Method for Estimating Indicator functions 401 11 The Support Vector Method for Estimating Real-Valued functions 443 12 SV Machines for pattern Recognition 493 13 SV Machines for Function Approximations, Regression Estimation, and Signal Processing 521 III Statistical Foundation of Learning Theory 14 Necessary and Sufficient Conditions for Uniform Convergence of Frequencies to their Probabilities 571 15 Necessary and Sufficient Conditions for Uniform Convergence of Means to their Expectations 597 16 Necessary and Sufficient Conditions for Uniform One-sided Convergence of Means to their Expectations 629 Comments and Bibliographical Remarks 681 References 723 Index 733

「Nielsen BookData」 より

詳細情報

• NII書誌ID(NCID): BA38258797
• ISBN:
  • 0471030031
  • 9780471030034
• LCCN: 97037075
• 出版国コード: us
• タイトル言語コード: eng
• 本文言語コード: eng
• 出版地: New York
• ページ数/冊数: xxiv, 736 p.
• 大きさ: 25 cm
• 分類:
  • LCC : Q325.7
  • DC21 : 008.3/1
• 件名:
  • LCSH : Computational learning theory
• 親書誌ID:
  • BA22396962