Abstract
A type of problem domains known as pseudo-independent (PI) models poses difficulty for common learning methods, which are based on the single-link lookahead search. To learn this type of domain models, a method called the multiple-link lookahead search is needed. An improved result can be obtained by incorporating model complexity into a scoring metric to explicitly trade off model accuracy for complexity and vice versa during selection of the best model among candidates at each learning step. Previous studies found the complexity formulae for full PI models (the simplest type of PI models) and for atomic PI models (PI models without submodels). This study presents the complexity formula for non-atomic PI models, which are more complex than full or atomic PI models, yet more general. Together with the previous results, this study completes the major theoretical work for the new learning algorithm that combines complexity and accuracy.
Keywords
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsPreview
Unable to display preview. Download preview PDF.
References
Chickering, D., Geiger, D., Heckerman, D.: Learning Bayesian networks: search methods and experimental results. In: Proceedings of 5th Conference on Artificial Intelligence and Statistics, Ft. Lauderdale, pp. 112–128. Society for AI and Statistics,
Cooper, G.F., Herskovits, E.: A Bayesian method for the induction of probabilistic networks from data. Machine Learning 9, 309–347 (1992)
Friedman, N., Murphy, K., Russell, S.: Learning the structure of dynamic probabilistic networks. In: Cooper, G.F., Moral, S. (eds.) Proceedings of 14th Conference on Uncertainty in Artificial Intelligence, Madison, Wisconsin, pp. 139–147. Morgan Kaufmann, San Francisco (1998)
Heckerman, D., Geiger, D., Chickering, D.M.: Learning Bayesian networks: the combination of knowledge and statistical data. Machine Learning 20, 197–243 (1995)
Lam, W., Bacchus, F.: Learning Bayesian networks: an approach based on the MDL principle. Computational Intelligence 10(3), 269–293 (1994)
Lee, J., Xiang, Y.: Model complexity of pseudo-independent models. In: Proceedings of 16th Florida Artificial Intelligence Research Society Conference (2005) (Forthcoming)
Xiang, Y.: Towards understanding of pseudo-independent domains. In: Poster Proceedings of 10th International Symposium on Methodologies for Intelligent Systems, Charlotte (1997)
Xiang, Y., Hu, J., Cercone, N., Hamilton, H.: Learning pseudo-independent models: analytical and experimental results. In: Hamilton, H. (ed.) Advances in Artificial Intelligence, pp. 227–239. Springer, Heidelberg (2000)
Xiang, Y., Lee, J.: Local score computation in learning belief networks. In: Stroulia, E., Matwin, S. (eds.) Advances in Artificial Intelligence, pp. 152–161. Springer, Heidelberg (2001)
Xiang, Y., Lee, J., Cercone, N.: Parameterization of pseudo-independent models. In: Proceedings of 16th Florida Artificial Intelligence Research Society Conference, St. Augustine, pp. 521–525 (2003)
Xiang, Y., Lee, J., Cercone, N.: Towards better scoring metrics for pseudo-independent models. International Journal of Intelligent Systems 20 (2004)
Xiang, Y., Wong, S.K.M., Cercone, N.: Critical remarks on single link search in learning belief networks. In: Proceedings of 12th Conference on Uncertainty in Artificial Intelligence, Portland, pp. 564–571 (1996)
Xiang, Y., Wong, S.K.M., Cercone, N.: A ‘microscopic’ study of minimum entropy search in learning decomposable Markov networks. Machine Learning 26(1), 65–92 (1997)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2005 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Lee, JH. (2005). Foundation for the New Algorithm Learning Pseudo-Independent Models. In: Godo, L. (eds) Symbolic and Quantitative Approaches to Reasoning with Uncertainty. ECSQARU 2005. Lecture Notes in Computer Science(), vol 3571. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11518655_17
Download citation
DOI: https://doi.org/10.1007/11518655_17
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-27326-4
Online ISBN: 978-3-540-31888-0
eBook Packages: Computer ScienceComputer Science (R0)