On the relationship between dependence tree classification error and Bayes error rate

Kiran S. Balagani, Vir V. Phoha

Research output: Contribution to journalArticle

2 Scopus citations

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(ω X). We derive the correct expansion of H(ω X) and present its implication.

Original languageEnglish (US)
Pages (from-to)1866-1868
Number of pages3
JournalIEEE Transactions on Pattern Analysis and Machine Intelligence
Volume29
Issue number10
DOIs
StatePublished - Dec 1 2007

Keywords

  • Bayes error rate
  • Classification
  • Dependence tree approximation
  • Entropy
  • Mutual information

ASJC Scopus subject areas

  • Software
  • Computer Vision and Pattern Recognition
  • Computational Theory and Mathematics
  • Artificial Intelligence
  • Applied Mathematics

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