Constructively learning a near-minimal neural network architecture

Justin Fletcher, Zoran Obradovic

Research output: Chapter in Book/Report/Conference proceedingConference contribution

2 Scopus citations

Abstract

Rather than iteratively manually examining a variety of pre-specified architectures, a constructive learning algorithm dynamically creates a problem-specific neural network architecture. Here we present an revised version of our parallel constructive neural network learning algorithm which constructs such an architecture. The three steps of searching for points on separating hyperplanes, determining separating hyperplanes from separating points and selecting separating hyperplanes generate a near-minimal architecture. As expected, experimental results indicate improved network generalization.

Original languageEnglish (US)
Title of host publicationIEEE International Conference on Neural Networks - Conference Proceedings
PublisherIEEE
Pages204-208
Number of pages5
Volume1
Publication statusPublished - 1994
Externally publishedYes
EventProceedings of the 1994 IEEE International Conference on Neural Networks. Part 1 (of 7) - Orlando, FL, USA
Duration: Jun 27 1994Jun 29 1994

Other

OtherProceedings of the 1994 IEEE International Conference on Neural Networks. Part 1 (of 7)
CityOrlando, FL, USA
Period6/27/946/29/94

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ASJC Scopus subject areas

  • Software

Cite this

Fletcher, J., & Obradovic, Z. (1994). Constructively learning a near-minimal neural network architecture. In IEEE International Conference on Neural Networks - Conference Proceedings (Vol. 1, pp. 204-208). IEEE.