NDLI: Asymptotic statistical theory of overtraining and cross-validation

Content Provider	IEEE Xplore Digital Library
Author	Amari, S. Murata, N. Muller, K.-R. Finke, M. Yang, H.H.
Copyright Year	1990
Abstract	A statistical theory for overtraining is proposed. The analysis treats general realizable stochastic neural networks, trained with Kullback-Leibler divergence in the asymptotic case of a large number of training examples. It is shown that the asymptotic gain in the generalization error is small if we perform early stopping, even if we have access to the optimal stopping time. Based on the cross-validation stopping we consider the ratio the examples should be divided into training and cross-validation sets in order to obtain the optimum performance. Although cross-validated early stopping is useless in the asymptotic region, it surely decreases the generalization error in the nonasymptotic region. Our large scale simulations done on a CM5 are in good agreement with our analytical findings.
Sponsorship	IEEE Computational Intelligence Society
Starting Page	985
Ending Page	996
Page Count	12
File Size	522952
File Format	PDF
ISSN	10459227
Volume Number	8
Issue Number	5
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	1997-09-01
Publisher Place	U.S.A.
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Neural networks Physics Stochastic processes Performance gain Large-scale systems Analytical models Terrorism Risk management
Content Type	Text
Resource Type	Article
Subject	Artificial Intelligence Computer Networks and Communications Computer Science Applications Software

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