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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

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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},
  month_numeric = {5}
}