On the Relationship Between Dependence Tree Classification Error and Bayes Error Rate
IEEE Transactions on Pattern Analysis and Machine Intelligence
Labeling of Human Motion by Constraint-Based Genetic Algorithm
Computational Intelligence and Security
Criteria to evaluate approximate belief network representations in expert systems
Decision Support Systems
An entropy-based learning algorithm of Bayesian conditional trees
UAI'92 Proceedings of the Eighth international conference on Uncertainty in artificial intelligence
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C.K. Chow and C.N. Liu (1968) introduced the notion of three dependence to approximate a kth-order probability distribution. More recently, A.K.C. Wong and C.C. Wang (1977) proposed a different product approximation. The present authors show that the tree dependence approximation suggested by Chow and Liu can be derived by minimizing an upper bound of the Bayes error rate under certain assumptions. They also show that the method proposed by Wong and Wang does not necessarily lead to fewer misclassifications, because it is a special case of such a minimization procedure.