Exact Upper and Lower Bounds on the Misclassification Probability

DOI10.1109/TIT.2019.2891529zbMATH Open1432.60031arXiv1712.00812OpenAlexW2787254544WikidataQ128607813 ScholiaQ128607813MaRDI QIDQ5224075

Iosif Pinelis

Publication date: 19 July 2019

Published in: IEEE Transactions on Information Theory (Search for Journal in Brave)

Abstract: Exact lower and upper bounds on the best possible misclassification probability for a finite number of classes are obtained in terms of the total variation norms of the differences between the sub-distributions over the classes. These bounds are compared with the exact bounds in terms of the conditional entropy obtained by Feder and Merhav.

Full work available at URL: https://arxiv.org/abs/1712.00812

Mathematics Subject Classification ID

Classification and discrimination; cluster analysis (statistical aspects) (62H30) Inequalities; stochastic orderings (60E15) Measures of information, entropy (94A17)

Related Items (4)

Misclassification minimization ⋮ Estimation of misclassification rate in the Asymptotic Rare and Weak model with sub-Gaussian noises ⋮ Classification with guaranteed probability of error ⋮ Graph-Theoretic Concepts in Computer Science

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