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  Semi-supervised Learning via Generalized Maximum Entropy

Erkan, A., & Altun, Y. (2010). Semi-supervised Learning via Generalized Maximum Entropy. In Y. Teh, & M. Titterington (Eds.), JMLR Workshop and Conference Proceedings (pp. 209-216). Cambridge, MA, USA: JMLR.

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 Creators:
Erkan, AN1, 2, Author              
Altun, Y1, 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: Various supervised inference methods can be analyzed as convex duals of the generalized maximum entropy (MaxEnt) framework. Generalized MaxEnt aims to find a distribution that maximizes an entropy function while respecting prior information represented as potential functions in miscellaneous forms of constraints and/or penalties. We extend this framework to semi-supervised learning by incorporating unlabeled data via modifications to these potential functions reflecting structural assumptions on the data geometry. The proposed approach leads to a family of discriminative semi-supervised algorithms, that are convex, scalable, inherently multi-class, easy to implement, and that can be kernelized naturally. Experimental evaluation of special cases shows the competitiveness of our methodology.

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 Dates: 2010-05
 Publication Status: Published in print
 Pages: -
 Publishing info: -
 Table of Contents: -
 Rev. Type: -
 Identifiers: URI: http://www.aistats.org/aistats2010/
BibTex Citekey: 6622
 Degree: -

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Title: Thirteenth International Conference on Artificial Intelligence and Statistics (AI & Statistics 2010)
Place of Event: Chia Laguna Resort, Italy
Start-/End Date: 2010-05-13 - 2010-05-15

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Title: JMLR Workshop and Conference Proceedings
Source Genre: Proceedings
 Creator(s):
Teh, YW, Editor
Titterington, M, Editor
Affiliations:
-
Publ. Info: Cambridge, MA, USA : JMLR
Pages: - Volume / Issue: 9 Sequence Number: - Start / End Page: 209 - 216 Identifier: -