Heuristics for the Extraction of Rules from Discrete-Time Recurrent Neural Networks

Proceedings of the International Joint Conference on Neural Networks

Volumn 1, Issue , 1992, Pages 33-38

  Omlin, C W ^a   Giles, C L ^b   Miller, C B ^b  

^a RENSSELAER POLYTECHNIC INSTITUTE   (United States)

^b NEC LABORATORIES AMERICA   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

FORMAL LANGUAGES; HEURISTIC METHODS;

CLUSTERING HEURISTICS; DISCRETE TIME RECURRENT NEURAL NETWORK; FINITE SET; GENERALIZATION PERFORMANCE; LANGUAGE RECOGNITION; LEARN+; NEGATIVE EXAMPLES; NEURAL-NETWORKS; POSITIVE EXAMPLES; RECURRENT STATE;

RECURRENT NEURAL NETWORKS;

EID: 0347870513     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/IJCNN.1992.287212     Document Type: Conference Paper

References (15)

1. D. Angluin, C.H. Smith, Inductive Inference: Theory and Practice, ACM Computing Surveys, Vol. 15, no. 3, p. 237 (1983).
2. A. Cleeremans, D. Servan-Schreiberand J. McClelland, Finite State Automata and Simple Recurrent Recurrent Networks, Neural Computation, Vol. 1, no. 3, p. 372 (1989).
3. J.P. Crutchfield, K. Young, Computational the Onset of Chaos, Proceedings of the 1988 Workshop on Complexity, Entropy and the Physics of Information, W.H. Zurek (ed), Addison-Wesley, Redwood City, Ca., p. 223, (1991).
4. J.L. Elman, Finding Structure in Time, Cognitive Science, Vol. 14, p. 179, (1990).
5. S.E. Fahlman, The Recurrent Cascade-Correlation Architecture, Advances in Neural Information Processing Systems S, R.P. Lippman, J.E. Moody, D.S. Touretzky (eds), Morgan Kaufmann, San Mateo, Ca., p. 190, (1991).
6. K.S. Fu, Syntactic Pattern Recognition and Applications, Prentice Hall, Englewood Cliffs, N.J. (1982).
7. C.L. Giles, G.Z. Sun, H.H. Chen, Y.C. Lee, D. Chen, Higher Order Recurrent Networks & Grammatical Inference, Advances in Neural Information Systems S, D.S. Touretzky (ed), Morgan Kaufmann, San Mateo, Ca, p. 380, (1990).
8. C.L. Giles, C.B. Miller, D. Chen, H.H. Chen, G.Z. Sun, Y.C. Lee, Learning and Extracting Finite State Automata with Second-Order Recurrent Neural Networks, to appear in Neural Computation.
9. E.M. Gold, Complexity of Automaton Identification from Given Data, Information and Control, Vol. 37, p. 302 (1978).
10. J.E. Hopcroft, J.F. Ullman, Introduction to Automata Theory, Languages and Computation, Addison-Wesley, Reading, Mass. (1979).
11. Y.C. Lee, G. Doolen, H.H. Chen, G.Z. Sun, T. Maxwell, H.Y. Lee, C.L. Giles, Machine Learning Using a Higher Order Correlational Network, Physica D, Vol. 22-D, no. 1-3, p. 276 (1986).
12. J.B. Pollack, The Induction of Dynamical Recognizers, Machine Learning, Kluwer Academic Publishers, Boston, Vol. 7, p. 227 (1991).
13. G.Z. Sun, H.H. Chen, C.L. Giles, Y.C. Lee and D. Chen, Connectionist Pushdown Automata that Learn Context-Free Grammars, Proceedings of the International Joint Conference on Neural Networks, IJCNN-90-WASH-DC, Lawrence Erlbaum, Hillsdale, N.J., Vol. 1, p. 577 (1990).
14. M. Tomita, Dynamic Construction of Finite-State Automata from Examples using Hill-Climbing, Proceedings of the Fourth Annual Cognitive Science Conference, p. 105 (1982).
15. R.J. Williams, D. Zipser, A Learning Algorithm for Continually Running Fully Recurrent Neural Networks, Neural Computation, Vol. 1, no. 2, p. 270, (1989).