MLP classifiers: Overtraining and solutions

Research output: Chapter in book / Conference proceedingConference article published in proceeding or bookAcademic researchpeer-review

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, we first discuss the overtraining problem based on an artificial two-input two-category classification problem. We then suggest five solutions to the overtraining problem, which are supported by the experimental results.
Original languageEnglish
Title of host publicationIEEE International Conference on Neural Networks - Conference Proceedings
PublisherIEEE
Pages2821-2824
Number of pages4
Publication statusPublished - 1 Dec 1995
EventProceedings of the 1995 IEEE International Conference on Neural Networks. Part 1 (of 6) - Perth, Australia
Duration: 27 Nov 19951 Dec 1995

Conference

ConferenceProceedings of the 1995 IEEE International Conference on Neural Networks. Part 1 (of 6)
CountryAustralia
CityPerth
Period27/11/951/12/95

ASJC Scopus subject areas

  • Software

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