PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

Learning with Local and Global Consistency
Dengyong Zhou, Olivier Bousquet, Thomas Navin Lal, Jason Weston and Bernhard Schölkopf
In: NIPS 2003, Vancouver, Canada(2004).


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.

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EPrint Type:Conference or Workshop Item (Paper)
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Computational, Information-Theoretic Learning with Statistics
Learning/Statistics & Optimisation
Theory & Algorithms
ID Code:476
Deposited By:Dengyong Zhou
Deposited On:23 December 2004