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  Discrete vs. Continuous: Two Sides of Machine Learning

Zhou, D. (2004). Discrete vs. Continuous: Two Sides of Machine Learning. Talk presented at IBM Watson Research Center. Yorktown Heights, NY, USA. 2004-10-12.

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Item Permalink: http://hdl.handle.net/11858/00-001M-0000-0013-B46E-3 Version Permalink: http://hdl.handle.net/21.11116/0000-0005-66E3-8
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 problem of transductive inference. In many real-world problems, unlabeled data is far easier to obtain than labeled data. Hence transductive inference is very significant in many practical problems. According to Vapnik's point of view, one should predict the function value only on the given points directly rather than a function defined on the whole space, the latter being a more complicated problem. Inspired by this idea, we develop discrete calculus on finite discrete spaces, and then build discrete regularization. A family of transductive algorithms is naturally derived from this regularization framework. We validate the algorithms on both synthetic and real-world data from text/web categorization to bioinformatics problems. A significant by-product of this work is a powerful way of ranking data based on examples including images, documents, proteins and many other kinds of data. This talk is mainly based on the followiing contribution: (1) D. Zhou and B. Schölkopf: Transductive Inference with Graphs, MPI Technical report, August, 2004; (2) D. Zhou, B. Schölkopf and T. Hofmann. Semi-supervised Learning on Directed Graphs. NIPS 2004; (3) D. Zhou, O. Bousquet, T.N. Lal, J. Weston and B. Schölkopf. Learning with Local and Global Consistency. NIPS 2003.

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 Dates: 2004-10
 Publication Status: Published online
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 Identifiers: BibTex Citekey: 2902
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Title: IBM Watson Research Center
Place of Event: Yorktown Heights, NY, USA
Start-/End Date: 2004-10-12
Invited: Yes

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