An entropy-based learning algorithm of Bayesian conditional trees

Authors:
Dan Geiger
Affiliations:
Computer Science Department, Technion-Israel Institute of Technology, Haifa, Israel
Venue:
UAI'92 Proceedings of the Eighth international conference on Uncertainty in artificial intelligence
Year:
1992

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Abstract

This article offers a modification of Chow and Liu's learning algorithm in the context of handwritten digit recognition. The modified algorithm directs the user to group digits into several classes consisting of digits that are hard to distinguish and then constructing an optimal conditional tree representation for each class of digits instead of for each single digit as done by Chow and Liu (1968). Advantages and extensions of the new method are discussed. Related works of Wong and Wang (1977) and Wong and Poon (1989) which offer a different entropy-based learning algorithm are shown to rest on inappropriate assumptions.