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H. 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.
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. 34. J. L. Devore, Probability and Statistics for Engineering and the Sciences, Monterey, CA: Brooks/Cole Pub. , 1982. 35. B. W. Wah, A. Ieumwananonthachai, and T. Yu, Genetics-based learning and statistical generalization, in S. , 1997. 36. P. Mehra and B. W. Wah, A systematic method for automated learning of load-balancing strategies in distributed systems, Int.
Note that PWIN(i) is independent of subdomain j and can be used in generalization if it were true across all subdomains, even those subdomains that were not tested in learning. The following are three possible outcomes when comparing PWIN(i) of Hi to H0. ARTIFICIAL INTELLIGENCE, GENERALIZATION 1. Hi is the only hypothesis that is better than H0 in all subdomains. Hi can then be chosen as the hypothesis for generalization. 2. Multiple hypotheses are better than H0 in all subdomains. Here, we should select one hypothesis that maximizes the likelihood of being better than H0 over the entire domain.
31.Knowledge and Data Engineering by John G. Webster (Editor)