Training multilayer perceptrons by principal component analysis

M. Biehl, C. Bunzmann and R. Urbanczik

Physica A: Statistical Mechanics and its Applications, 2001, vol. 302, issue 1, 56-63

Abstract: We present a training algorithm for multilayer perceptrons which relates to the technique of principal component analysis. The latter is performed with respect to a correlation matrix which is computed from the example inputs and their target outputs. For large networks the novel procedure requires far fewer examples for good generalization than traditional on-line algorithms.

Keywords: Neural networks; Machine learning; Disordered systems (search for similar items in EconPapers)
Date: 2001
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Persistent link: https://EconPapers.repec.org/RePEc:eee:phsmap:v:302:y:2001:i:1:p:56-63

DOI: 10.1016/S0378-4371(01)00440-X

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