Efficient Approximation of the Conditional Relative Entropy with Applications to Discriminative Learning of Bayesian Network Classifiers

We propose a minimum variance unbiased approximation to the conditional relative entropy of the distribution induced by the observed frequency estimates, for multi-classification tasks. Such approximation is an extension of a decomposable scoring criterion, named approximate conditional log-likeliho...

Deskribapen osoa

Xehetasun bibliografikoak

Egile Nagusiak:	Paulo Mateus, Pedro Adão, Alexandra M. Carvalho
Formatua:	Artigo
Hizkuntza:	Inglês
Argitaratua:	MDPI AG 2013-07-01
Saila:	Entropy
Gaiak:	conditional relative entropy approximation discriminative learning Bayesian network classifiers
Sarrera elektronikoa:	http://www.mdpi.com/1099-4300/15/7/2716
Etiketak:	Etiketa erantsi Etiketarik gabe, Izan zaitez lehena erregistro honi etiketa jartzen!