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Cluster Kernels for Semi-Supervised Learning

2003

Conference Paper

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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

Department(s): Empirical Inference
Bibtex Type: Conference Paper (inproceedings)

Event Name: 16th Annual Conference on Neural Information Processing Systems (NIPS 2002)
Event Place: Vancouver, BC, Canada

Address: Cambridge, MA, USA
Digital: 0
ISBN: 0-262-02550-7
Organization: Max-Planck-Gesellschaft
School: Biologische Kybernetik

Links: PDF
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BibTex

@inproceedings{2051,
  title = {Cluster Kernels for Semi-Supervised Learning},
  author = {Chapelle, O. and Weston, J. and Sch{\"o}lkopf, B.},
  journal = {Advances in Neural Information Processing Systems},
  booktitle = {Advances in Neural Information Processing Systems 15},
  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},
  month_numeric = {10}
}