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

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Zhou,  D
Department Empirical Inference, Max Planck Institute for Biological Cybernetics, Max Planck Society;
Max Planck Institute for Biological Cybernetics, Max Planck Society;

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Zhou, D. (2004). Learning from Labeled and Unlabeled Data: Semi-supervised Learning and Ranking. Talk presented at The Natural Language Computing Group of Microsoft Research Asia, and the Institute of System Sciences, the Chinese Academy of Sciences. Beijing, China.


Cite as: https://hdl.handle.net/11858/00-001M-0000-0013-B474-4
Abstract
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.