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  Supervised Feature Selection via Dependence Estimation

Song, L., Smola, A., Gretton, A., Borgwardt, K., & Bedo, J. (2007). Supervised Feature Selection via Dependence Estimation. In Twenty-Fourth Annual International Conference on Machine Learning (ICML 2007) (pp. 823-830). New York, NY, USA: ACM Press.

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Item Permalink: https://hdl.handle.net/11858/00-001M-0000-0013-CD6B-B Version Permalink: https://hdl.handle.net/11858/00-001M-0000-0013-CD6C-9
Genre: Conference Paper

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 Creators:
Song, L, Author
Smola, AJ1, Author           
Gretton, A2, Author           
Borgwardt, KM1, Author           
Bedo, J, Author
Ghahramani, Z., Editor
Affiliations:
1Max Planck Institute for Biological Cybernetics, Max Planck Society, ou_1497794              
2Department Empirical Inference, Max Planck Institute for Biological Cybernetics, Max Planck Society, ou_1497795              

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 Abstract: We introduce a framework for filtering features that employs the Hilbert-Schmidt Independence Criterion (HSIC) as a measure of dependence between the features and the labels. The key idea is that good features should maximise such dependence. Feature selection for various supervised learning problems (including classification and regression) is unified under this framework, and the solutions can be approximated using a backward-elimination algorithm. We demonstrate the usefulness of our method on both artificial and real world datasets.

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 Dates: 2007-06
 Publication Status: Issued
 Pages: -
 Publishing info: -
 Table of Contents: -
 Rev. Type: -
 Identifiers: ISBN: 978-1-59593-793-3
URI: http://oregonstate.edu/conferences/icml2007/
DOI: 10.1145/1273496.1273600
BibTex Citekey: 4462
 Degree: -

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Title: Twenty-Fourth Annual International Conference on Machine Learning (ICML 2007)
Place of Event: Corvallis, OR, USA
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Title: Twenty-Fourth Annual International Conference on Machine Learning (ICML 2007)
Source Genre: Proceedings
 Creator(s):
Affiliations:
Publ. Info: New York, NY, USA : ACM Press
Pages: - Volume / Issue: - Sequence Number: - Start / End Page: 823 - 830 Identifier: -