John G. Webster (Editor) 's 31.Knowledge and Data Engineering PDF

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This is similar to first normalizing the performance with respect to that of the baseline and formulating a constraint such that the normalized performance is larger than one. Again, care must be taken in normalization because anomalies in performance ordering may happen when certain normalization methods are used and the baseline is changed. Probabilities of mean have been used to evaluate generalizability in various genetics-based learning and generalization experiments (17,32,33,35,36,37,38,39).

Barron, Approximation and estimation bounds for artificial neural networks, in Proc. 4th Annual Workshop on Computational Learning Theory (COLT’91), pages 243–249, Palo Alto, CA, Morgan Kaufmann, 1991. 32. B. W. , Genetics-based learning of new heuristics: rational scheduling of experiments and generalization, IEEE Trans. Knowl. , 7: 763–785, 1995. 33. A. Ieumwananonthachai and B. W. Wah, Statistical generalization of performance-related heuristics for knowledge-lean applications, Int. J. Artificial Intelligence Tools, 5 (1 2): 61–79, June 1996.

Holland, Adaptation in Natural and Artificial Systems, Ann Arbor, MI: Univ. of Michigan Press, 1975. 8. L. B. Booker, D. E. Goldberg, and J. H. Holland, Classifier systems and genetic algorithms, Artificial Intelligence, 40 (1): 235– 282, 1989. 692 ARTIFICIAL LIMBS 9. J. L. McClelland and D. E. Rumelhart, Parallel Distributed Processing: Explorations in the Microstructure of Cognition, Volume 1: Foundations, Cambridge, MA: Bradford Books (MIT Press), 1985. 10. G. F. DeJong and R. J. Mooney, Explanation-based learning: an alternative view, Machine Learning, 1 (2): 145–176, 1986.

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31.Knowledge and Data Engineering by John G. Webster (Editor)


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