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Conference Paper

Semi-supervised Learning via Generalized Maximum Entropy

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Erkan,  AN
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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Altun,  Y
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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Citation

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.


Cite as: http://hdl.handle.net/11858/00-001M-0000-0013-C04E-A
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.