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Learning with Local and Global Consistency

2004

Conference Paper

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We consider the general problem of learning from labeled and unlabeled data, which is often called semi-supervised learning or transductive inference. A principled approach to semi-supervised learning is to design a classifying function which is sufficiently smooth with respect to the intrinsic structure collectively revealed by known labeled and unlabeled points. We present a simple algorithm to obtain such a smooth solution. Our method yields encouraging experimental results on a number of classification problems and demonstrates effective use of unlabeled data.

Author(s): Zhou, D. and Bousquet, O. and Lal, TN. and Weston, J. and Schölkopf, B.
Book Title: Advances in Neural Information Processing Systems 16
Journal: Advances in Neural Information Processing Systems
Pages: 321-328
Year: 2004
Month: June
Day: 0
Editors: S Thrun and LK Saul and B Sch{\"o}lkopf
Publisher: MIT Press

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

Event Name: 17th Annual Conference on Neural Information Processing Systems (NIPS 2003)
Event Place: Vancouver, BC, Canada

Address: Cambridge, MA, USA
Digital: 0
Institution: Max Planck Institute for Biological Cybernetics
ISBN: 0-262-20152-6
Organization: Max-Planck-Gesellschaft
School: Biologische Kybernetik

Links: PDF
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BibTex

@inproceedings{2333,
  title = {Learning with Local and Global Consistency},
  author = {Zhou, D. and Bousquet, O. and Lal, TN. and Weston, J. and Sch{\"o}lkopf, B.},
  journal = {Advances in Neural Information Processing Systems},
  booktitle = {Advances in Neural Information Processing Systems 16},
  pages = {321-328},
  editors = {S Thrun and LK Saul and B Sch{\"o}lkopf},
  publisher = {MIT Press},
  organization = {Max-Planck-Gesellschaft},
  institution = {Max Planck Institute for Biological Cybernetics},
  school = {Biologische Kybernetik},
  address = {Cambridge, MA, USA},
  month = jun,
  year = {2004},
  month_numeric = {6}
}