Some experimental results on learning probabilistic and possibilistic networks with different evaluation measures-Reference-Cited by-同舟云学术

Some experimental results on learning probabilistic and possibilistic networks with different evaluation measures

Published:1997 Issue: Volume: Page:71-85
ISSN:0302-9743
Container-title:Qualitative and Quantitative Practical Reasoning
language:
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Author:

Borgelt Christian,Kruse Rudolf

Publisher

Springer Berlin Heidelberg

Link

http://link.springer.com/content/pdf/10.1007/BFb0035613

Reference31 articles.

1. S.K. Andersen, K.G. Olesen, F.V. Jensen, and F. Jensen. HUGIN — A shell for building Bayesian belief universes for expert systems. Proc. 11th Int. J. Conf. on Artificial Intelligence, 1080–1085, 1989

2. L. Breiman, J.H. Friedman, R.A. Olshen, and C.J. Stone. Classification and Regression Trees, Wadsworth International Group, Belmont, CA, 1984

3. C.K. Chow and C.N. Liu. Approximating Discrete Probability Distributions with Dependence Trees. IEEE Trans. on Information Theory 14(3):462–467, IEEE 1968

4. G.F. Cooper and E. Herskovits. A Bayesian Method for the Induction of Probabilistic Networks from Data. Machine Learning 9:309–347, Kluwer 1992

5. Z. Daróczy. Generalized Information Functions. Information and Control 16:36–51, 1970

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