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J. Weston, R. Collobert, F. Sinz , L. Bottou and V. Vapnik. Inference with the Universum. Proceedings of the Twenty-third International Conference on Machine Learning (ICML 2006), IMLS/ICML, 2006.

Abstract

In this paper we study a new framework introduced by Vapnik (1998; 2006) that is an alternative capacity concept to the large margin approach. In the particular case of binary classification, we are given a set of labeled examples, and a collection of rage the Universum by maximizing the number of observed contradictions, and show experimentally that this approach delivers accuracy improvements over using labeled data alone.

BibTeX

@inproceedings{weston:2006,
 author = {J. Weston and R. Collobert and F. Sinz  and L. Bottou and V. Vapnik},
 title = {Inference with the Universum},
 booktitle = {Proceedings of the Twenty-third International Conference on Machine Learning (ICML 2006)},
 year = {2006},
 pages = {1009--1016},
 location = {Pittsburgh, Pennsylvania},
 publisher = {ACM Press}
}

Inference with the Universum

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