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NIPS
2003

Learning with Local and Global Consistency

9 years 9 months ago
Learning with Local and Global Consistency
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.
Dengyong Zhou, Olivier Bousquet, Thomas Navin Lal,
Added 31 Oct 2010
Updated 31 Oct 2010
Type Conference
Year 2003
Where NIPS
Authors Dengyong Zhou, Olivier Bousquet, Thomas Navin Lal, Jason Weston, Bernhard Schölkopf
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