A training algorithm for optimal margin classifiers

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A training algorithm for optimal margin classifiers

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Annual Workshop on Computational Learning Theory archive
Proceedings of the fifth annual workshop on Computational learning theory table of contents

Pittsburgh, Pennsylvania, United States

Pages: 144 - 152

Year of Publication: 1992

ISBN:0-89791-497-X

Authors

Bernhard E. Boser	EECS Department, University of California, Berkeley, CA
Isabelle M. Guyon	AT&T Bell Laboratories, 50 Fremont Street, 6th Floor, San Francisco, CA
Vladimir N. Vapnik	AT&T Bell Laboratories, Crawford Corner Road, Holmdel, NJ

Sponsors

SIGACT: ACM Special Interest Group on Algorithms and Computation Theory
SIGART: ACM Special Interest Group on Artificial Intelligence

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ACM New York, NY, USA

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Downloads (6 Weeks): 64, Downloads (12 Months): 692, Citation Count: 276

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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 the classification 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.

REFERENCES

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