The learning dynamics of a universal approximator

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

The learning dynamics of a universal approximator

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

Research output: Contribution to journal › Article › peer-review

3 Citations (Scopus)

79 Downloads (Pure)

Abstract

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 language	English
Pages (from-to)	288-294
Number of pages	7
Journal	Advances in Neural Information Processing Systems
Volume	9
Publication status	Published - May 1997

Bibliographical note

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

Keywords

approximator
back-propagation
symmetric phases
realizable cases
noiseless data

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The learning dynamics of a universal approximator
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abstract = "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.",

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journal = "Advances in Neural Information Processing Systems",

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A2 - Jordan, Michael I.

N1 - Copyright of the Massachusetts Institute of Technology Press (MIT Press)

PY - 1997/5

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N2 - 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.

AB - 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.

KW - approximator

KW - back-propagation

KW - symmetric phases

KW - realizable cases

KW - noiseless data

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JO - Advances in Neural Information Processing Systems

JF - Advances in Neural Information Processing Systems

ER -

The learning dynamics of a universal approximator

Abstract

Bibliographical note

Keywords

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