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  Inference with the Universum

Weston, J., Collobert, R., Sinz, F., Bottou, L., & Vapnik, V. (2006). Inference with the Universum. In W. Cohen, & A. Moore (Eds.), ICML '06: Proceedings of the 23rd International Conference on Machine Learning (pp. 1009-1016). New York, NY, USA: ACM Press.

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Item Permalink: https://hdl.handle.net/11858/00-001M-0000-0013-D139-2 Version Permalink: https://hdl.handle.net/21.11116/0000-0004-9A81-C
Genre: Conference Paper

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https://dl.acm.org/citation.cfm?doid=1143844.1143971 (Publisher version)
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 Creators:
Weston, J, Author           
Collobert, R, Author
Sinz, F1, 2, Author           
Bottou, L, Author
Vapnik, V, Author           
Affiliations:
1Research Group Computational Vision and Neuroscience, Max Planck Institute for Biological Cybernetics, Max Planck Society, ou_1497805              
2Max 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
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 Rev. Type: -
 Identifiers: DOI: 10.1145/1143844.1143971
BibTex Citekey: 3916
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Title: 23rd International Conference on Machine Learning (ICML 2006)
Place of Event: Pittsburgh, PA, USA
Start-/End Date: 2006-06-25 - 2006-06-29

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Title: ICML '06: Proceedings of the 23rd International Conference on Machine Learning
Source Genre: Proceedings
 Creator(s):
Cohen, W, Editor
Moore, A, Editor
Affiliations:
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Publ. Info: New York, NY, USA : ACM Press
Pages: - Volume / Issue: - Sequence Number: - Start / End Page: 1009 - 1016 Identifier: ISBN: 1-59593-383-2