Incremental learning with rule-based neural networks

C. M. Higgins, R. M. Goodman

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

18 Scopus citations


A classifier for discrete-valued variable classification problems is presented. The system utilizes an information-theoretic algorithm for constructing informative rules from example data. These rules are then used to construct a neural network to perform parallel inference and posterior probability estimation. The network can be grown incrementally, so that new data can be incorporated without repeating the training on previous data. It is shown that this technique performs as well as other techniques such as backpropagation while having unique advantages in incremental learning capability, training efficiency, knowledge representation, and hardware implementation suitability.

Original languageEnglish (US)
Title of host publicationProceedings. IJCNN-91-Seattle
Subtitle of host publicationInternational Joint Conference on Neural Networks
Editors Anon
PublisherPubl by IEEE
Number of pages6
ISBN (Print)0780301641
StatePublished - 1991
EventInternational Joint Conference on Neural Networks - IJCNN-91-Seattle - Seattle, WA, USA
Duration: Jul 8 1991Jul 12 1991

Publication series

NameProceedings. IJCNN-91-Seattle: International Joint Conference on Neural Networks


OtherInternational Joint Conference on Neural Networks - IJCNN-91-Seattle
CitySeattle, WA, USA

ASJC Scopus subject areas

  • General Engineering


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