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Perceptron capacity revisited: classification ability for correlated patterns

Takashi Shinzato and Yoshiyuki Kabashima

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In this paper, we address the problem of how many randomly labeled patterns can be correctly classified by a single-layer perceptron when the patterns are correlated with each other. In order to solve this problem, two analytical schemes are developed based on the replica method and the Thouless–Anderson–Palmer (TAP) approach by utilizing an integral formula concerning random rectangular matrices. The validity and relevance of the developed methodologies are shown for one known result and two example problems. A message-passing algorithm to perform the TAP scheme is also presented.


PACS

84.35.+i Neural networks

07.05.Mh Neural networks, fuzzy logic, artificial intelligence

MSC

82C32 Neural nets (See also 68T05, 91E40, 92B20)

15A52 Random matrices

68T05 Learning and adaptive systems (See also 68Q32, 91E40)

Subjects

Electronics and devices

Instrumentation and measurement

Dates

Issue 32 (15 August 2008)

Received 25 December 2007, in final form 27 February 2008

Published 30 July 2008



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