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  Multi-Task Feature Selection on Multiple Networks via Maximum Flows

Sugiyama, M., Azencott, C.-A., Grimm, D., Kawahara, Y., & Borgwardt, K. M. (2014). Multi-Task Feature Selection on Multiple Networks via Maximum Flows. In M. Zaki (Ed.), Proceedings of the 2014 SIAM International Conference on Data Mining (pp. 199-207). Society for Industrial and Applied Mathematics (SIAM). doi:10.1137/1.9781611973440.23.

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 Creators:
Sugiyama, M., Author
Azencott, C.-A.1, Author           
Grimm, D.2, Author           
Kawahara, Y., Author
Borgwardt, K. M.1, Author           
Affiliations:
1Research Group Machine Learning and Computational Biology, Max Planck Institute for Intelligent Systems, Max Planck Society, ou_1497664              
2Dept. Empirical Inference, Max Planck Institute for Intelligent System, Max Planck Society, ou_1497647              

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Free keywords: Abt. Schölkopf
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 Dates: 20142014
 Publication Status: Issued
 Pages: -
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 Table of Contents: -
 Rev. Type: -
 Identifiers: DOI: 10.1137/1.9781611973440.23
BibTex Citekey: SugiyamaAGKB2014
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Title: 2014 SIAM International Conference on Data Mining
Place of Event: Philadelphia, PA, USA
Start-/End Date: 2014-04-24 - 2014-04-26

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Title: Proceedings of the 2014 SIAM International Conference on Data Mining
Source Genre: Proceedings
 Creator(s):
Zaki, M., Editor
Obradovic, Z., Author
Ning Tan, P., Author
Banerjee, A., Author
Kamath, C., Author
Parthasarathy, S., Author
Affiliations:
-
Publ. Info: Society for Industrial and Applied Mathematics (SIAM)
Pages: 1086 Volume / Issue: - Sequence Number: - Start / End Page: 199 - 207 Identifier: ISBN: 978-1-61197-344-0