Fitting the Smallest Enclosing Bregman Ball Export

@incollection{citeulike:1460621, abstract = {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 B\u{a}doiu and Clarkson in 2002.}, author = {Nock, Richard and Nielsen, Frank}, citeulike-article-id = {1460621}, citeulike-linkout-0 = {http://dx.doi.org/10.1007/11564096_65}, doi = {10.1007/11564096_65}, journal = {Machine Learning: ECML 2005}, keywords = {approximation, bregman, exponential-families, frank-nielsen, kullback-leibler, minmax}, pages = {649--656}, posted-at = {2007-07-17 05:33:19}, priority = {5}, title = {Fitting the Smallest Enclosing Bregman Ball}, url = {http://dx.doi.org/10.1007/11564096_65}, year = {2005} }

TY - CHAP ID - citeulike:1460621 L3 - citeulike-article-id:1460621 N2 - 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. JF - Machine Learning: ECML 2005 EP - 656 TI - Fitting the Smallest Enclosing Bregman Ball SP - 649 KW - approximation KW - bregman KW - exponential-families KW - frank-nielsen KW - kullback-leibler KW - minmax AU - Nock, Richard AU - Nielsen, Frank PY - 2005/// UR - http://dx.doi.org/10.1007/11564096_65 ER -