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Learning Nonoverlapping Perceptron Networks from Examples and Membership Queries.

, , and . Mach. Learn., 16 (3): 161-183 (1994)

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Simple recurrent networks as generalized hidden Markov models with distributed representations., and . ICNN, page 979-984. IEEE, (1995)On learning simple deterministic and probabilistic neural concepts., and . EuroCOLT, page 47-60. Oxford University Press, (1993)Average Case Analysis of the Clipped Hebb Rule for Nonoverlapping Perception Networks., and . COLT, page 151-157. ACM, (1993)On Learning Perceptrons with Binary Weights., and . Neural Comput., 5 (5): 767-782 (1993)Average Case Analysis of an Hebb-Type Rule that Finds the Network Connectivity.. Int. J. Neural Syst., 5 (2): 115-122 (1994)Stochastic simple recurrent neural networks., , and . ICGI, volume 1147 of Lecture Notes in Computer Science, page 262-273. Springer, (1996)The truth is in there: current issues in extracting rules from trained feedforward artificial neural networks., , , and . ICNN, page 2530-2534. IEEE, (1997)On learning ?-perceptron networks on the uniform distribution., , and . Neural Networks, 9 (1): 67-82 (1996)On Learning µ-Perceptron Networks with Binary Weights., , and . NIPS, page 591-598. Morgan Kaufmann, (1992)The truth will come to light: directions and challenges in extracting the knowledge embedded within trained artificial neural networks., , , and . IEEE Trans. Neural Networks, 9 (6): 1057-1068 (1998)