On-line learning in multilayer neural networks

David Saad; Sara A. Solla

doi:10.1007/978-1-4615-6099-9_53

On-line learning in multilayer neural networks

David Saad, Sara A. Solla

Research output: Chapter in Book/Published conference output › Chapter

Abstract

We present an analytic solution to the problem of on-line gradient-descent learning for two-layer neural networks with an arbitrary number of hidden units in both teacher and student networks. The technique, demonstrated here for the case of adaptive input-to-hidden weights, becomes exact as the dimensionality of the input space increases.

Original language	English
Title of host publication	Proceedings of the first international conference on mathematics of neural networks : models, algorithms and applications: models, algorithms and applications
Place of Publication	Oxford
Publisher	Kluwer
ISBN (Print)	0-7923-99331
DOIs	https://doi.org/10.1007/978-1-4615-6099-9_53
Publication status	Published - 1997

Bibliographical note

Keywords

algorithm
design
measurement
performance
theory
verification

Access to Document

10.1007/978-1-4615-6099-9_53

misc96-004.pdf
© Springer Science+Business Media New York 1997

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AB - We present an analytic solution to the problem of on-line gradient-descent learning for two-layer neural networks with an arbitrary number of hidden units in both teacher and student networks. The technique, demonstrated here for the case of adaptive input-to-hidden weights, becomes exact as the dimensionality of the input space increases.

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KW - design

KW - measurement

KW - performance

KW - theory

KW - verification

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BT - Proceedings of the first international conference on mathematics of neural networks : models, algorithms and applications: models, algorithms and applications

PB - Kluwer

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ER -

On-line learning in multilayer neural networks

Abstract

Bibliographical note

Keywords

Access to Document

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