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Statistical Learning Theory / Edition 1

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Overview

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.

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Editorial Reviews

Booknews
Devoted to the theory that explores ways of estimating functional dependency from a given collection of data, a problem that engages several important topics of classical statistics: discriminant analysis, regression analysis, and the density estimation problem. Considers a new paradigm, the learning paradigm, which has been developed over the past 30 years. The first two parts comprise a textbook for a graduate course on the subject for students in statistics, mathematics, engineering, physics, or computer science; the third part is at a higher level of sophistication and could be used in a special course on empirical processes for Ph.D. students in mathematics and statistics. Annotation c. by Book News, Inc., Portland, Or.
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Product Details

Table of Contents

Partial table of contents:

THEORY OF LEARNING AND GENERALIZATION.

Two Approaches to the Learning Problem.

Estimation of the Probability Measure and Problem of Learning.

Conditions for Consistency of Empirical Risk Minimization Principle.

The Structural Risk Minimization Principle.

Stochastic Ill-Posed Problems.

SUPPORT VECTOR ESTIMATION OF FUNCTIONS.

Perceptrons and Their Generalizations.

SV Machines for Function Approximations, Regression Estimation, and Signal Processing.

STATISTICAL FOUNDATION OF LEARNING THEORY.

Necessary and Sufficient Conditions for Uniform Convergence of Frequencies to Their Probabilities.

Necessary and Sufficient Conditions for Uniform One-Sided Convergence of Means to Their Expectations.

Comments and Bibliographical Remarks.

References.

Index.

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