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  Inference with the Universum

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.

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Item Permalink: https://hdl.handle.net/11858/00-001M-0000-0013-D139-2 Version Permalink: https://hdl.handle.net/11858/00-001M-0000-0013-D13A-F
Genre: Conference Paper

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 Creators:
Weston, J1, Author           
Collobert R, Sinz, F2, Author           
Bottou, L, Author
Vapnik, V3, Author           
Cohen A. Moore, W. W., Editor
Affiliations:
1Department Empirical Inference, Max Planck Institute for Biological Cybernetics, Max Planck Society, ou_1497795              
2Research Group Computational Vision and Neuroscience, Max Planck Institute for Biological Cybernetics, Max Planck Society, ou_1497805              
3Max Planck Institute for Biological Cybernetics, Max Planck Society, ou_1497794              

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 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.

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 Dates: 2006-06
 Publication Status: Issued
 Pages: -
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 Table of Contents: -
 Rev. Type: -
 Identifiers: URI: http://portal.acm.org/citation.cfm?id=1143971jmp=abstractcoll=GUIDEdl=GUIDECFID=1380099CFTOKEN=72073021abstract
DOI: 10.1145/1143844.1143971
BibTex Citekey: 3916
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Title: 23rd International Conference on Machine Learning
Place of Event: Pittsburgh, PA, USA
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Title: Proceedings of the 23rd International Conference on Machine Learning (ICML 2006)
Source Genre: Journal
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Publ. Info: New York, NY, USA : ACM Press
Pages: - Volume / Issue: - Sequence Number: - Start / End Page: 1009 - 1016 Identifier: -