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Learning from Labeled and Unlabeled Data: Semi-supervised Learning and Ranking




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.
Year: 2004
Month: January
Day: 0

Department(s): Empirical Inference
Bibtex Type: Talk (talk)

Digital: 0
Event Place: The Natural Language Computing Group of Microsoft Research Asia, and the Institute of System Sciences, the Chinese Academy of Sciences, Beijing, China
Organization: Max-Planck-Gesellschaft
School: Biologische Kybernetik

Links: PDF


  title = {Learning from Labeled and Unlabeled Data: Semi-supervised Learning and Ranking},
  author = {Zhou, D.},
  organization = {Max-Planck-Gesellschaft},
  school = {Biologische Kybernetik},
  month = jan,
  year = {2004},
  month_numeric = {1}