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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Detaylı Bibliyografya
Asıl Yazarlar: Bao-Gang Hu, Hong-Jie Xing
Materyal Türü: Makale
Dil:English
Baskı/Yayın Bilgisi: MDPI AG 2016-02-01
Seri Bilgileri:Entropy
Konular:
Online Erişim:http://www.mdpi.com/1099-4300/18/2/59