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

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http://pubman.mpdl.mpg.de/cone/persons/resource/persons84330

Zhou,  D
Department Empirical Inference, 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: http://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.