R. Maclin & J. Shavlik (1993).
Using Knowledge-based Neural Networks To Improve Algorithms: Refining the Chou-Fasman Algorithm for Protein Folding. Machine Learning, 11, pp. 195-215.

This publication is available remotely and available in postscript.

Abstract:

This paper describes a connectionist method for refining algorithms represented as generalized finite-state automata. The method translates the rule-like knowledge in an automaton into a corresponding artificial neural network, and then refines the reformulated automaton by applying backpropagation to a set of examples. This technique for translating an automaton into a network extends the KBANN algorithm, a system that translates a set of propositional rules into a corresponding neural network. The extended system, FSKBANN, allows one to refine the large class of algorithms that can be represented as state-based processes. As a test, FSKBANN is used to improve the Chou-Fasman algorithm, a method for predicting how globular proteins fold. Empirical evidence shows that the multistrategy approach of FSKBANN leads to a statistically significantly more accurate solution than both the original Chou-Fasman algorithm and a neural network trained using the standard approach. Extensive statistics report the types of errors made by the Chou-Fasman algorithm, the standard neural network, and by the FSKBANN network.

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