On the Relationship Between Dependence Tree Classification Error and Bayes Error Rate

Authors:
Kiran S. Balagani;Vir V. Phoha
Affiliations:
-;-
Venue:
IEEE Transactions on Pattern Analysis and Machine Intelligence
Year:
2007

Citing 3
Cited 0

Comments on Approximating Discrete Probability Distributions with Dependence Trees

IEEE Transactions on Pattern Analysis and Machine Intelligence
Elements of information theory

Elements of information theory
Arbitrarily Tight Upper and Lower Bounds on the Bayesian Probability of Error

IEEE Transactions on Pattern Analysis and Machine Intelligence

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Abstract

Wong and Poon [1] showed that Chow and Liu’s tree dependence approximation can be derived by minimizing an upper bound of the Bayes error rate. Wong and Poon’s result was obtained by expanding the conditional entropy H(w|X). We derive the correct expansion of H(w|X) and present its implication.