@inproceedings{2005-C-BregmanBall-ECML
, author={Richard Nock and Frank Nielsen}
, title={Fitting the smallest enclosing Bregman ball}
, booktitle={European Conference on Machine Learning (ECML)}
, month={October}
, year={2005}
, pages={649-656}
, address={Porto, Portugal}
, doi={10.1007/11564096_65}
, 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 Badoiu and Clarkson in 2002. 
}
}