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Empirical Inference Conference Paper 2003

Cluster Kernels for Semi-Supervised Learning

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Empirical Inference
Bernhard Schölkopf
  • Director
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Empirical Inference
Jason Weston
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Empirical Inference
Olivier Chapelle

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.

Author(s): Chapelle, O. and Weston, J. and Schölkopf, B.
Book Title: Advances in Neural Information Processing Systems 15
Journal: Advances in Neural Information Processing Systems
Pages: 585-592
Year: 2003
Month: October
Day: 0
Editors: S Becker and S Thrun and K Obermayer
Publisher: MIT Press
Bibtex Type: Conference Paper (inproceedings)
Address: Cambridge, MA, USA
Event Name: 16th Annual Conference on Neural Information Processing Systems (NIPS 2002)
Event Place: Vancouver, BC, Canada
Digital: 0
Electronic Archiving: grant_archive
ISBN: 0-262-02550-7
Organization: Max-Planck-Gesellschaft
School: Biologische Kybernetik
Links:

BibTex 

@inproceedings{2051,
  title = {Cluster Kernels for Semi-Supervised Learning},
  journal = {Advances in Neural Information Processing Systems},
  booktitle = {Advances in Neural Information Processing Systems 15},
  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.},
  pages = {585-592},
  editors = {S Becker and S Thrun and K Obermayer},
  publisher = {MIT Press},
  organization = {Max-Planck-Gesellschaft},
  school = {Biologische Kybernetik},
  address = {Cambridge, MA, USA},
  month = oct,
  year = {2003},
  slug = {2051},
  author = {Chapelle, O. and Weston, J. and Sch{\"o}lkopf, B.},
  month_numeric = {10}
}