Refining Domain Theories Expressed as Finite-State Automata

Richard Maclin, Jude W. Shavlik

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

8 Scopus citations

Abstract

The kbann system uses neural networks to refine domain theories. Currently, domain knowledge in kbann is expressed as nonrecursive, propositional rules. We extend kbann to domain theories expressed as finite-state automata. We apply finite-state KBANN to the task of predicting how proteins fold, producing a small but statistically significant gain in accuracy over both a standard neural network approach and a non-learning algorithm from the biological literature. Our method shows promise at solving this and other real-world problems that can be described in terms of statedependent decisions.

Original languageEnglish (US)
Title of host publicationProceedings of the 8th International Workshop on Machine Learning, ICML 1991
EditorsLawrence A. Birnbaum, Gregg C. Collins
PublisherMorgan Kaufmann Publishers, Inc.
Pages524-528
Number of pages5
ISBN (Electronic)1558602003, 9781558602007
DOIs
StatePublished - 1991
Externally publishedYes
Event8th International Workshop on Machine Learning, ICML 1991 - Evanston, United States
Duration: Jun 1 1991 → …

Publication series

NameProceedings of the 8th International Workshop on Machine Learning, ICML 1991

Conference

Conference8th International Workshop on Machine Learning, ICML 1991
Country/TerritoryUnited States
CityEvanston
Period6/1/91 → …

Bibliographical note

Publisher Copyright:
© ICML 1989.All rights reserved

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