Supervised learning from clustered input examples.

C. Maeangi, M. Biehl, S. A. Solla

Research output: Contribution to journalArticlepeer-review

11 Scopus citations


In this paper we analyse the effect of introducing a structure in the input distribution on the generalization ability of a simple perceptron. The simple case of two clusters of input data and a linearly separable rule is considered. We find that the generalization ability improves with the separation between the clusters, and is bounded from below by the result for the unstructured case, recovered as the separation between clusters vanishes. The asymptotic behaviour for large training sets, however, is the same for structured and unstructured input distributions. For small training sets, the dependence of the generalization error on the number of examples is observed to be non-monotonic for certain values of the model parameters.

Original languageEnglish (US)
Pages (from-to)117-122
Number of pages6
Issue number2
StatePublished - Apr 10 1995

ASJC Scopus subject areas

  • Physics and Astronomy(all)


Dive into the research topics of 'Supervised learning from clustered input examples.'. Together they form a unique fingerprint.

Cite this