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Machine Learning Proceedings 1991
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 atdoi:10.1016/b978-1-55860-200-7.50107-0 dblp:conf/icml/MaclinS91 fatcat:u6vjzvxys5h2jfj4dxahmqfkdi