Complexity measurement of fundamental pseudo-independent models
International Journal of Approximate Reasoning
RSFDGrC'05 Proceedings of the 10th international conference on Rough Sets, Fuzzy Sets, Data Mining, and Granular Computing - Volume Part I
Foundation for the new algorithm learning pseudo-independent models
ECSQARU'05 Proceedings of the 8th European conference on Symbolic and Quantitative Approaches to Reasoning with Uncertainty
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Learning belief networks from data is NP-hard in general. A common method used in heuristic learning is the single-link lookahead search. When the problem domain is pseudo-independent (PI), the method cannot discover the underlying probabilistic model. In learning these models, to explicitly trade model accuracy and model complexity, parameterization of PI models is necessary. Understanding of PI models also provides a new dimension of trade-off in learning even when the underlying model may not be PI. In this work, we adopt a hypercube perspective to analyze PI models and derive an improved result for computing the maximum number of parameters needed to specify a full PI model. We also present results on parameterization of a subclass of partial PI models. © 2004 Wiley Periodicals, Inc. Int J Int Syst 19: 749–768, 2004.