Extreme entropy machines: robust information theoretic classification

Extreme entropy machines: robust information theoretic classification

Pattern Analysis and Applications

2017

Most existing classification methods are aimed at minimization of empirical risk (through some simple point-based error measured with loss function) with added regularization. We propose to approach the classification problem by applying entropy measures as a model objective function. We focus on quadratic Renyi’s entropy and connected Cauchy–Schwarz Divergence which leads to the construction of extreme entropy machines (EEM). The main contribution of this paper is proposing a model based on the

383–400

20

10.1007/s10044-015-0497-8

http://link.springer.com/article/10.1007/s10044-015-0497-8?wt_mc=email.event.1.SEM.ArticleAuthorOnlineFirst