Fitting the Smallest Enclosing Bregman Ball
نویسندگان
چکیده
Finding a point which minimizes the maximal distortion with respect to a dataset is an important estimation problem that has recently received growing attentions in machine learning, with the advent of one class classification. We propose two theoretically founded generalizations to arbitrary Bregman divergences, of a recent popular smallest enclosing ball approximation algorithm for Euclidean spaces coined by Bădoiu and Clarkson in 2002.
منابع مشابه
Fitting the smallest enclosing Bregman balls
Finding a point which minimizes the maximal distortion with respect to a dataset is an important estimation problem that has recently received growing attentions in machine learning, with the advent of one class classification. In this paper, we study the problem from a general standpoint, and suppose that the distortion is a Bregman divergence, without restriction. Applications of this formula...
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تاریخ انتشار 2005