Learning with regularizers in multilayer neural networks

David Saad, Magnus Rattray

Research output: Contribution to journalArticlepeer-review


We study the effect of regularization in an on-line gradient-descent learning scenario for a general two-layer student network with an arbitrary number of hidden units. Training examples are randomly drawn input vectors labelled by a two-layer teacher network with an arbitrary number of hidden units which may be corrupted by Gaussian output noise. We examine the effect of weight decay regularization on the dynamical evolution of the order parameters and generalization error in various phases of the learning process, in both noiseless and noisy scenarios.
Original languageEnglish
Pages (from-to)2170-2176
Number of pages7
JournalPhysical Review E
Issue number2
Publication statusPublished - Feb 1998

Bibliographical note

Copyright of the American Physical Society


  • on-line gradient-descent learning scenario
  • Gaussian
  • noise
  • weight decay
  • error


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