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Conference Paper

Inference with the Universum

MPS-Authors
http://pubman.mpdl.mpg.de/cone/persons/resource/persons84311

Weston,  J
Department Empirical Inference, Max Planck Institute for Biological Cybernetics, Max Planck Society;

http://pubman.mpdl.mpg.de/cone/persons/resource/persons84226

Collobert R, Sinz,  F
Research Group Computational Vision and Neuroscience, Max Planck Institute for Biological Cybernetics, Max Planck Society;

http://pubman.mpdl.mpg.de/cone/persons/resource/persons84987

Vapnik,  V
Max Planck Institute for Biological Cybernetics, Max Planck Society;

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Citation

Weston, J., Collobert R, Sinz, F., Bottou, L., & Vapnik, V. (2006). Inference with the Universum. Proceedings of the 23rd International Conference on Machine Learning (ICML 2006), 1009-1016.


Cite as: http://hdl.handle.net/11858/00-001M-0000-0013-D139-2
Abstract
WIn this paper we study a new framework introduced by Vapnik (1998) and Vapnik (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.