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

By John G. Webster (Editor)

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D. Nguyen and B. Widrow, The truck backer-upper: An example of self-learning in neural networks, in Proc. Int’l Joint Conf. on Neural Networks, volume II, pages 357–363. IEEE, 1989. 17. -C. Teng and B. W. Wah, Automated learning of the minimal configuration of a feed forward neural network, IEEE Trans. , 7: 1072–1085, 1996. 18. W. W. Cohen, Generalizing number and learning from multiple examples in explanation based learning, Machine Learning, pages 256–269, 1988. 19. B. W. Wah, Population-based learning: a new method for learning from examples under resource constraints, IEEE Trans.

28. A. , A general lower bound on the number of examples needed for learning. In D. Haussler and L. ), Proc. 1988 Workshop on Computational Learning Theory, pages 139–154, Palo Alto, CA, Morgan Kaufmann, 1988. 29. E. B. Baum and D. Haussler, What size net gives valid generalization? In D. Z. ), Proc. Neural Information Processing Systems, pages 81–90, New York, American Inst. of Physics, 1988. 30. D. Haussler, Generalizing the PAC model: Sample size bounds from metric dimension-based uniform convergence results, in Proc.

A different approach to precomputation is taken by Darwiche and Provan (21), where networks are converted into sets of precomputed rules, one set of rules per type of query. There are many forms of approximate inference. Approximate techniques tend to generate approximate results using user-controlled bounds on the amount of time used to do the inference. The most commonplace are techniques based on Monte Carlo sampling, Gibbs sampling, or logic sampling. The basis of these techniques is to use the belief network as a generator of random samples, check how many times the desired cases show up in the random sample, and from that compute the probabilities of those cases.

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