DocumentCode
296021
Title
MLP classifiers: overtraining and solutions
Author
Chi, Zheru
Author_Institution
Dept. of Electron. Eng., Hong Kong Polytech., Hung Hom, Hong Kong
Volume
5
fYear
1995
fDate
Nov/Dec 1995
Firstpage
2821
Abstract
Training a multi-layer perceptron (MLP) classifier is difficult to control and as a result its performance on unseen patterns is unpredicted. Overtraining is one of many problems in training an MLP classifier. In this paper, the author first discusses the overtraining problem based on an artificial two-input two-category classification problem. The author then suggests five solutions to the overtraining problem, which are supported by experimental results
Keywords
learning (artificial intelligence); multilayer perceptrons; pattern classification; multi-layer perceptron classifier; overtraining; two-input two-category classification problem; Degradation; Electronic mail; Multilayer perceptrons; Pattern recognition; Testing;
fLanguage
English
Publisher
ieee
Conference_Titel
Neural Networks, 1995. Proceedings., IEEE International Conference on
Conference_Location
Perth, WA
Print_ISBN
0-7803-2768-3
Type
conf
DOI
10.1109/ICNN.1995.488180
Filename
488180
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