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 “non-examples” that do not belong to either class of interest. This collection, called the Universum, allows one to encode prior knowledge by representing meaningful concepts in the same domain as the problem at hand. We describe an algorithm to leverage the Universum by maximizing the number of observed contradictions, and show experimentally that this approach delivers accuracy improvements over using labeled data alone.
@inproceedings{weston-collobert-sinz-bottou-vapnik-2006,
author = {Weston, Jason and Collobert, Ronan and Sinz, Fabian and Bottou, L\'{e}on and Vapnik, Vladimir},
title = {Inference with the Universum},
year = {2006},
booktitle = {Proceedings of the Twenty-third International Conference on Machine Learning (ICML 2006)},
publisher = {IMLS/ICML},
note = {ACM Digital Library},
url = {http://leon.bottou.org/papers/weston-collobert-sinz-bottou-vapnik-2006},
}