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

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.

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Item Permalink: https://hdl.handle.net/11858/00-001M-0000-0013-B474-4 Version Permalink: https://hdl.handle.net/21.11116/0000-0005-67F4-4
Genre: Talk

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 Creators:
Zhou, D1, 2, Author           
Affiliations:
1Department Empirical Inference, Max Planck Institute for Biological Cybernetics, Max Planck Society, ou_1497795              
2Max Planck Institute for Biological Cybernetics, Max Planck Society, Spemannstrasse 38, 72076 Tübingen, DE, ou_1497794              

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 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.

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 Dates: 2004-01
 Publication Status: Published online
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 Identifiers: BibTex Citekey: 2589
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Title: The Natural Language Computing Group of Microsoft Research Asia, and the Institute of System Sciences, the Chinese Academy of Sciences
Place of Event: Beijing, China
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Invited: Yes

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