Adaptive regularization of neural classifiers

Author

Andersen, L. Nonboe ; Larsen, J. ; Hansen, L.K. ; Hintz-Madsen, M.

Author_Institution

Dept. of Math. Modelling, Tech. Univ., Lyngby, Denmark

fYear

1997

fDate

24-26 Sep 1997

Firstpage

24

Lastpage

33

Abstract

We present a regularization scheme which iteratively adapts the regularization parameters by minimizing the validation error. It is suggested to use the adaptive regularization scheme in conjunction with optimal brain damage pruning to optimize the architecture and to avoid overfitting. Furthermore, we propose an improved neural classification architecture eliminating an inherent redundancy in the widely used SoftMax classification network. Numerical results demonstrate the viability of the method

Keywords

adaptive signal processing; iterative methods; neural net architecture; pattern classification; SoftMax classification network; adaptive regularization; architecture optimization; iterative adaptation; neural classification architecture; neural classifiers; optimal brain damage pruning; validation error minimization; Assembly; Bayesian methods; Biological neural networks; Feedforward systems; Mathematical model; Neurons; Pattern recognition; Probability; Redundancy; World Wide Web;

fLanguage

English

Publisher

ieee

Conference_Titel

Neural Networks for Signal Processing [1997] VII. Proceedings of the 1997 IEEE Workshop

Conference_Location

Amelia Island, FL

ISSN

1089-3555

Print_ISBN

0-7803-4256-9

Type

conf

DOI

10.1109/NNSP.1997.622380

Filename

622380