Adaptive learning algorithms to incorporate additional functional constraints into neural networks

نویسندگان

  • So-Young Jeong
  • Soo-Young Lee
چکیده

In this paper, adaptive learning algorithms to obtain better generalization performance are proposed. We speci"cally designed cost terms for the additional functionality based on the "rstand second-order derivatives of neural activation at hidden layers. In the course of training, these additional cost functions penalize the input-to-output mapping sensitivity and highfrequency components in training data. A gradient-descent method results in hybrid learning rules to combine the error back-propagation, Hebbian rules, and the simple weight decay rules. However, additional computational requirements to the standard error back-propagation algorithm are almost negligible. Theoretical justi"cations and simulation results are given to verify the e!ectiveness of the proposed learning algorithms. ( 2000 Elsevier Science B.V. All rights reserved.

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عنوان ژورنال:
  • Neurocomputing

دوره 35  شماره 

صفحات  -

تاریخ انتشار 2000