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          Institute: MPI für biologische Kybernetik     Collection: Biologische Kybernetik     Display Documents



ID: 548494.0, MPI für biologische Kybernetik / Biologische Kybernetik
Semi-supervised Learning via Generalized Maximum Entropy
Authors:Erkan, A.N.; Altun, Y.
Editors:Teh, Y. W.; Titterington, M.
Date of Publication (YYYY-MM-DD):2010-05
Title of Proceedings:Proceedings of the Thirteenth International Conference on Artificial Intelligence and Statistics (AISTATS 2010)
Start Page:209
End Page:216
Physical Description:8
Audience:Not Specified
Intended Educational Use:No
Abstract / Description: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.
External Publication Status:published
Document Type:Conference-Paper
Communicated by:Holger Fischer
Affiliations:MPI für biologische Kybernetik/Empirical Inference (Dept. Schölkopf)
Identifiers:LOCALID:6622
URL:http://www.aistats.org/
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