Maximum Margin Semi-Supervised Learning for Structured Variables
2006
Conference Paper
ei
Many real-world classification problems involve the prediction of multiple inter-dependent variables forming some structural dependency. Recent progress in machine learning has mainly focused on supervised classification of such structured variables. In this paper, we investigate structured classification in a semi-supervised setting. We present a discriminative approach that utilizes the intrinsic geometry of input patterns revealed by unlabeled data points and we derive a maximum-margin formulation of semi-supervised learning for structured variables. Unlike transductive algorithms, our formulation naturally extends to new test points.
Author(s): | Altun, Y. and McAllester, DA. and Belkin, M. |
Book Title: | Advances in neural information processing systems 18 |
Journal: | Advances in Neural Information Processing Systems 18: Proceedings of the 2005 Conference |
Pages: | 33-40 |
Year: | 2006 |
Month: | May |
Day: | 0 |
Editors: | Weiss, Y. , B. Sch{\"o}lkopf, J. Platt |
Publisher: | MIT Press |
Department(s): | Empirical Inference |
Bibtex Type: | Conference Paper (inproceedings) |
Event Name: | Nineteenth Annual Conference on Neural Information Processing Systems (NIPS 2005) |
Event Place: | Vancouver, BC, Canada |
Address: | Cambridge, MA, USA |
Digital: | 0 |
ISBN: | 0-262-23253-7 |
Language: | en |
Organization: | Max-Planck-Gesellschaft |
School: | Biologische Kybernetik |
Links: |
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BibTex @inproceedings{5706, title = {Maximum Margin Semi-Supervised Learning for Structured Variables}, author = {Altun, Y. and McAllester, DA. and Belkin, M.}, journal = {Advances in Neural Information Processing Systems 18: Proceedings of the 2005 Conference}, booktitle = {Advances in neural information processing systems 18}, pages = {33-40}, editors = {Weiss, Y. , B. Sch{\"o}lkopf, J. Platt}, publisher = {MIT Press}, organization = {Max-Planck-Gesellschaft}, school = {Biologische Kybernetik}, address = {Cambridge, MA, USA}, month = may, year = {2006}, doi = {}, month_numeric = {5} } |