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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 Z. Ghahramani (Ed.), ICML '07: 24th International Conference on Machine Learning (pp. 823-830). New York, NY, USA: ACM Press.

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Item Permalink: http://hdl.handle.net/11858/00-001M-0000-0013-CD6B-B Version Permalink: http://hdl.handle.net/21.11116/0000-0003-E2CA-A
Genre: Conference Paper

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ICML-2007-Song-Gretton.pdf (Any fulltext), 708KB
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 Creators:
Song, L, Author
Smola, AJ, Author              
Gretton, A1, 2, Author              
Borgwardt, KM, Author              
Bedo, J, Author
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: Published in print
 Pages: -
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 Table of Contents: -
 Rev. Type: -
 Identifiers: DOI: 10.1145/1273496.1273600
BibTex Citekey: 4462
 Degree: -

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Title: 24th Annual International Conference on Machine Learning (ICML 2007)
Place of Event: Corvallis, OR, USA
Start-/End Date: 2007-06-20 - 2007-06-24

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Title: ICML '07: 24th International Conference on Machine Learning
Source Genre: Proceedings
 Creator(s):
Ghahramani, Z, Editor
Affiliations:
-
Publ. Info: New York, NY, USA : ACM Press
Pages: - Volume / Issue: - Sequence Number: - Start / End Page: 823 - 830 Identifier: ISBN: 978-1-59593-793-3