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  Cluster Kernels for Semi-Supervised Learning

Chapelle, O., Weston, J., & Schölkopf, B. (2003). Cluster Kernels for Semi-Supervised Learning. Advances in Neural Information Processing Systems, 585-592.

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Item Permalink: https://hdl.handle.net/11858/00-001M-0000-0013-DB37-0 Version Permalink: https://hdl.handle.net/11858/00-001M-0000-0013-DB38-E
Genre: Conference Paper

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 Creators:
Chapelle, O1, Author           
Weston, J1, Author           
Schölkopf, B1, Author           
Becker, Editor
S., Editor
Thrun, S., Editor
Obermayer, K., Editor
Affiliations:
1Department Empirical Inference, Max Planck Institute for Biological Cybernetics, Max Planck Society, ou_1497795              

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 Abstract: We propose a framework to incorporate unlabeled data in kernel classifier, based on the idea that two points in the same cluster are more likely to have the same label. This is achieved by modifying the eigenspectrum of the kernel matrix. Experimental results assess the validity of this approach.

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 Dates: 2003-10
 Publication Status: Issued
 Pages: -
 Publishing info: -
 Table of Contents: -
 Rev. Type: -
 Identifiers: ISBN: 0-262-02550-7
URI: http://books.nips.cc/nips15.html
BibTex Citekey: 2051
 Degree: -

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Title: Sixteenth Annual Conference on Neural Information Processing Systems (NIPS 2002)
Place of Event: Vancouver, BC, Canada
Start-/End Date: -

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Title: Advances in Neural Information Processing Systems
Source Genre: Journal
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Affiliations:
Publ. Info: Cambridge, MA, USA : MIT Press
Pages: - Volume / Issue: - Sequence Number: - Start / End Page: 585 - 592 Identifier: -