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...

書誌詳細
主要な著者:	Bao-Gang Hu, Hong-Jie Xing
フォーマット:	論文
言語:	English
出版事項:	MDPI AG 2016-02-01
シリーズ:	Entropy
主題:	entropy error probability Bayesian errors error types upper bound lower bound
オンライン･アクセス:	http://www.mdpi.com/1099-4300/18/2/59

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