A training algorithm for optimal margin classifiers

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A training algorithm for optimal margin classifiers (1992)

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by Bernhard E. Boser, et al.

Venue:	PROCEEDINGS OF THE 5TH ANNUAL ACM WORKSHOP ON COMPUTATIONAL LEARNING THEORY
Citations:	1863 - 43 self

BibTeX

@INPROCEEDINGS{Boser92atraining,
    author = {Bernhard E. Boser and et al.},
    title = {A training algorithm for optimal margin classifiers},
    booktitle = {PROCEEDINGS OF THE 5TH ANNUAL ACM WORKSHOP ON COMPUTATIONAL LEARNING THEORY},
    year = {1992},
    pages = {144--152},
    publisher = {ACM Press}
}

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Abstract

A training algorithm that maximizes the margin between the training patterns and the decision boundary is presented. The technique is applicable to a wide variety of classifiaction functions, including Perceptrons, polynomials, and Radial Basis Functions. The effective number of parameters is adjusted automatically to match the complexity of the problem. The solution is expressed as a linear combination of supporting patterns. These are the subset of training patterns that are closest to the decision boundary. Bounds on the generalization performance based on the leave-one-out method and the VC-dimension are given. Experimental results on optical character recognition problems demonstrate the good generalization obtained when compared with other learning algorithms.

Keyphrases

training algorithm optimal margin classifier training pattern decision boundary leave-one-out method effective number generalization performance linear combination wide variety optical character recognition problem experimental result radial basis function good generalization classifiaction function

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