ML Anthology

Fitting the Smallest Enclosing Bregman Ball

Richard Nock, Frank Nielsen
ECML-PKDD 2005 pp. 649-656
doi:10.1007/11564096_65 /ecmlpkdd/2005/nock2005ecml-fitting/

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 Bădoiu and Clarkson in 2002.

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Cite

Text

Nock and Nielsen. "Fitting the Smallest Enclosing Bregman Ball." European Conference on Machine Learning, 2005. doi:10.1007/11564096_65

Markdown

[Nock and Nielsen. "Fitting the Smallest Enclosing Bregman Ball." European Conference on Machine Learning, 2005.](https://mlanthology.org/ecmlpkdd/2005/nock2005ecml-fitting/) doi:10.1007/11564096_65

BibTeX

@inproceedings{nock2005ecml-fitting,
  title     = {{Fitting the Smallest Enclosing Bregman Ball}},
  author    = {Nock, Richard and Nielsen, Frank},
  booktitle = {European Conference on Machine Learning},
  year      = {2005},
  pages     = {649-656},
  doi       = {10.1007/11564096_65},
  url       = {https://mlanthology.org/ecmlpkdd/2005/nock2005ecml-fitting/}
}