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Neural Network Learning

is surveyed, including a discussion of the relevance of the Vapnik-Chervonenkis dimension, and calculating estimates of the dimension for several;

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Neural Network Learning

is surveyed, including a discussion of the relevance of the Vapnik-Chervonenkis dimension, and calculating estimates of the dimension for several;

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Measures of Complexity

This book brings together historical notes, reviews of research developments, fresh ideas on how to make VC (Vapnik-Chervonenkis;

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Statistical Learning Theory

A comprehensive look at learning and generalization theory. The statistical theory of learning and g;...

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The Nature of Statistical Learning Theory

The aim of this book is to discuss the fundamental ideas which lie behind the statistical theory of ;...

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The Nature of Statistical Learning Theory

The aim of this book is to discuss the fundamental ideas which lie behind the statistical theory of ;...

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London Mathematical Society Lecture Note Series

these notions are easier to handle than in ordinary topology. The remarkable combinatorial property of o-minimal structures, the Vapnik;

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Uniform Central Limit Theorems

, an extended treatment of Vapnik-Chervonenkis combinatorics, the Ossiander L2 bracketing central limit theorem, the Gine-Zinn bootstrap;

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Machine Learning

paradigm and the important notion of the Vapnik-Chervonenkis dimension, the author explores specific topics such as finite automata and neural;

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Combinatorial Geometry

Distribution of distances among finitely many points Epsilon-nets and Vapnik--Chervonenkis dimension Geometric graph theory Geometric discrepancy;

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Advances in Domain Adaptation Theory

generalization guarantees, including sections on Vapnik-Chervonenkis (VC), Rademacher, PAC-Bayesian, Robustness and Stability based bounds. In addition;

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Support Vector Machines

during the last 15 years since Vapnik (1995, 1998) published his well-known textbooks on statistical learning theory with;

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Support Vector Machines

during the last 15 years since Vapnik (1995, 1998) published his well-known textbooks on statistical learning theory with;

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An Introduction to Computational Learning Theory

, which formalizes a relationship between learning and data compression; the Vapnik-Chervonenkis dimension; the equivalence of weak and strong;

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Data Mining and Statistics for Decision Making

regression, generalized linear models, regularized regression, PLS regression, decision trees, neural networks, support vector machines, Vapnik;

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