By John G. Webster (Editor)
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Several mechanisms have been developed to perform this inference, both exact and approximate. There are special cases where certain forms of the inference problem have been shown to be only polynomial in the size of the input (16). The general inference problem in this framework is, however, NPcomplete. In fact, both the exact inference problem and the approximation problem have been shown to be NP-complete. Because of the computational complexity of this problem, the goal in developing inference techniques has been to produce algorithms that work well in most cases, where working well must be measured both by execution time and accuracy.
For example, if one node in the belief network is a conditional Gaussian, all its children must be as well. A second influence on accuracy is the fact that learning systems will always be faced with sparse information. A onethousand petabyte database (1018 bytes) is not nearly large enough to fully characterize even a small fraction of the total probability distribution over a moderate-sized domain. For example, 100 variables with 10 values each gives rise to 10100 different events that could occur.
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. 11. T. M. Mitchell, R. M. Keller, and S. T. Kedar-Cabelli, Explanation-based generalization: a unifying view, Machine Learning, 1 (1): 47–80, 1986. 12. P. Mehra and B. W. ), Artificial Neural Networks: Concepts and Theory, Los Alamitos, CA: IEEE Computer Society Press, 1992.
31.Knowledge and Data Engineering by John G. Webster (Editor)