Titlebook: Learning from Data; Artificial Intellige Doug Fisher,Hans-J. Lenz Book 1996 Springer-Verlag New York, Inc. 1996 Bayesian network.Likelihood

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Ploxoma: Testbed for Uncertain Inferencevides results about more specific classes of patients. Nevertheless, a quantitative comparison reveals that the inferences sanctioned by Combinatorial Semantics are more reliable than those of belief function theory.

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On Test Selection Strategies for Belief Networksstrategies for both myopic and limited non-myopic (working with known test groups) test selection in the context of belief networks. We focus primarily on utility-free test selection strategies. However, the methods have immediate application to the decision-theoretic framework.

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A Hill-Climbing Approach for Optimizing Classification Treesich is usually represented by a binary classification tree. In this paper, we propose an efficient hill-climbing algorithm to search for the optimal or near-optimal classification trees. Computational results show that the hill-climbing approach was able to find optimal solutions for 95% of the cases tested.

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Paul R. Cohen,Dawn E. Gregory,Lisa Ballesteros,Robert St. Amanthis material must have a significant stiffness and strength to sustain mechanical loads. Crimping is performed to enhance the mechanical properties of mineral wool. As this process is governed by a lot of parameters one looks for ways to characterize the mechanical properties one-line. The aim of th