Semi-supervised Learning via Generalized Maximum Entropy
2010
Conference Paper
ei
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.
Author(s): | Erkan, AN. and Altun, Y. |
Book Title: | JMLR Workshop and Conference Proceedings Volume 9: AISTATS 2010 |
Journal: | Proceedings of the Thirteenth International Conference on Artificial Intelligence and Statistics (AISTATS 2010) |
Pages: | 209-216 |
Year: | 2010 |
Month: | May |
Day: | 0 |
Editors: | Teh, Y.W. , M. Titterington |
Publisher: | JMLR |
Department(s): | Empirical Inference |
Bibtex Type: | Conference Paper (inproceedings) |
Event Name: | Thirteenth International Conference on Artificial Intelligence and Statistics |
Event Place: | Chia Laguna Resort, Italy |
Address: | Cambridge, MA, USA |
Digital: | 0 |
Language: | en |
Organization: | Max-Planck-Gesellschaft |
School: | Biologische Kybernetik |
Links: |
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BibTex @inproceedings{6622, title = {Semi-supervised Learning via Generalized Maximum Entropy}, author = {Erkan, AN. and Altun, Y.}, journal = {Proceedings of the Thirteenth International Conference on Artificial Intelligence and Statistics (AISTATS 2010)}, booktitle = {JMLR Workshop and Conference Proceedings Volume 9: AISTATS 2010}, pages = {209-216}, editors = {Teh, Y.W. , M. Titterington}, publisher = {JMLR}, organization = {Max-Planck-Gesellschaft}, school = {Biologische Kybernetik}, address = {Cambridge, MA, USA}, month = may, year = {2010}, doi = {}, month_numeric = {5} } |