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Using the Perceptron Algorithm to Find Consistent Hypotheses

Published online by Cambridge University Press:  12 September 2008

Martin Anthony  and
John Shawe-Taylor
Martin Anthony
Affiliation:
Department of Statistical and Mathematical Sciences, London School of Economics, Houghton Street, London WC2A 2AE, UK. e-mail: anthony@vax.lse.ac.uk.
John Shawe-Taylor
Affiliation:
Department of Computer Science, Royal Holloway and Bedford New College Egham Hill, Egham, Surrey TW20 0EX, UK. e-mail: john@dcs.rhbnc.ac.uk.
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Abstract

The perceptron learning algorithm quite naturally yields an algorithm for finding a linearly separable boolean function consistent with a sample of such a function. Using the idea of a specifying sample, we give a simple proof that, in general, this algorithm is not efficient.

Information

Type
Research Article
Information
Combinatorics, Probability and Computing , Volume 2 , Issue 4 , December 1993 , pp. 385 - 387
Copyright
Copyright © Cambridge University Press 1993

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References

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