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Journal ArticleDOI

Learnability and the Vapnik-Chervonenkis dimension

TLDR
This paper shows that the essential condition for distribution-free learnability is finiteness of the Vapnik-Chervonenkis dimension, a simple combinatorial parameter of the class of concepts to be learned.
Abstract
Valiant's learnability model is extended to learning classes of concepts defined by regions in Euclidean space En. The methods in this paper lead to a unified treatment of some of Valiant's results, along with previous results on distribution-free convergence of certain pattern recognition algorithms. It is shown that the essential condition for distribution-free learnability is finiteness of the Vapnik-Chervonenkis dimension, a simple combinatorial parameter of the class of concepts to be learned. Using this parameter, the complexity and closure properties of learnable classes are analyzed, and the necessary and sufficient conditions are provided for feasible learnability.

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Citations
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Book

Boosting: Foundations and Algorithms

TL;DR: This book begins with a general introduction to machine learning algorithms and their analysis; then explores the core theory of boosting, especially its ability to generalize; examines some of the myriad other theoretical viewpoints that help to explain and understand boosting; provides practical extensions of boosting for more complex learning problems; and finally presents a number of advanced theoretical topics.
Journal ArticleDOI

Theory of classification : a survey of some recent advances

TL;DR: The last few years have witnessed important new developments in the theory and practice of pattern classification, see as discussed by the authors for a survey of the main new ideas that have lead to these important recent developments.
Journal ArticleDOI

Book review: Invitation to fixed-parameter algorithms

TL;DR: This is a review of the recent book by Rolf Niedermeier on fixed-parameter algorithms that contains a critical description of the covered material, a comparison with other books dedicated to the same field and an evaluation of its contribution as a monograph to the theoretical computer science community.
Proceedings ArticleDOI

How to use expert advice

TL;DR: This work analyzes algorithms that predict a binary value by combining the predictions of several prediction strategies, called `experts', and shows how this leads to certain kinds of pattern recognition/learning algorithms with performance bounds that improve on the best results currently known in this context.
References
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Book

Computers and Intractability: A Guide to the Theory of NP-Completeness

TL;DR: The second edition of a quarterly column as discussed by the authors provides a continuing update to the list of problems (NP-complete and harder) presented by M. R. Garey and myself in our book "Computers and Intractability: A Guide to the Theory of NP-Completeness,” W. H. Freeman & Co., San Francisco, 1979.
Book

The Art of Computer Programming

TL;DR: The arrangement of this invention provides a strong vibration free hold-down mechanism while avoiding a large pressure drop to the flow of coolant fluid.
Book

Pattern classification and scene analysis

TL;DR: In this article, a unified, comprehensive and up-to-date treatment of both statistical and descriptive methods for pattern recognition is provided, including Bayesian decision theory, supervised and unsupervised learning, nonparametric techniques, discriminant analysis, clustering, preprosessing of pictorial data, spatial filtering, shape description techniques, perspective transformations, projective invariants, linguistic procedures, and artificial intelligence techniques for scene analysis.