An Optimization Approach of Deriving Bounds between Entropy and Error from Joint Distribution: Case Study for Binary Classifications

An Optimization Approach of Deriving Bounds between Entropy and Error from Joint Distribution: Case Study for Binary Classifications

In this work, we propose a new approach of deriving the bounds between entropy and error from a joint distribution through an optimization means. The specific case study is given on binary classifications. Two basic types of classification errors are investigated, namely, the Bayesian and non-Bayesi...

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Autors principals:	Bao-Gang Hu, Hong-Jie Xing
Format:	Article
Idioma:	English
Publicat:	MDPI AG 2016-02-01
Col·lecció:	Entropy
Matèries:	entropy error probability Bayesian errors error types upper bound lower bound
Accés en línia:	http://www.mdpi.com/1099-4300/18/2/59

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