Learning from examples in large neural networks

H. Sompolinsky, N. Tishby, Hyunjune Sebastian Seung

Research output: Contribution to journalArticlepeer-review

105 Scopus citations


A statistical-mechanical theory of learning from examples in layered networks at finite temperature is studied. When the training error is a smooth function of continuously varying weights, the generalization error falls off asymptotically as the inverse number of examples. By analytical and numerical studies of single-layer perceptrons, we show that when the weights are discrete, the generalization error can exhibit a discontinuous transition to perfect generalization. For intermediate sizes of the example set, the state of perfect generalization coexists with a metastable spin-glass state.

Original languageEnglish (US)
Pages (from-to)1683-1686
Number of pages4
JournalPhysical review letters
Issue number13
StatePublished - 1990

All Science Journal Classification (ASJC) codes

  • General Physics and Astronomy


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