The learning dynamics of a universal approximator

Ansgar H. L. West, David Saad, Ian T. Nabney, Michael C. Mozer, Thomas Petsche, Michael I. Jordan

Research output: Contribution to journalArticle (Academic Journal)peer-review

3 Citations (Scopus)
14 Downloads (Pure)


The learning properties of a universal approximator, a normalized committee machine with adjustable biases, are studied for on-line back-propagation learning. Within a statistical mechanics framework, numerical studies show that this model has features which do not exist in previously studied two-layer network models without adjustable biases, e.g., attractive suboptimal symmetric phases even for realizable cases and noiseless data.
Original languageEnglish
Pages (from-to)288-294
Number of pages7
JournalAdvances in Neural Information Processing Systems
Publication statusPublished - 1 May 1997

Bibliographical note

Copyright of the Massachusetts Institute of Technology Press (MIT Press)


  • approximator, back-propagation, symmetric phases, realizable cases, noiseless data


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