Premium
Toward better scoring metrics for pseudo‐independent models
Author(s) -
Xiang Y.,
Lee J.,
Cercone N.
Publication year - 2004
Publication title -
international journal of intelligent systems
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.291
H-Index - 87
eISSN - 1098-111X
pISSN - 0884-8173
DOI - 10.1002/int.20023
Subject(s) - computer science , heuristic , dimension (graph theory) , artificial intelligence , hypercube , domain (mathematical analysis) , perspective (graphical) , machine learning , theoretical computer science , algorithm , mathematics , combinatorics , mathematical analysis , parallel computing
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.