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A Continuation Method for Semi-Supervised SVMs


Conference Paper


Semi-Supervised Support Vector Machines (S3VMs) are an appealing method for using unlabeled data in classification: their objective function favors decision boundaries which do not cut clusters. However their main problem is that the optimization problem is non-convex and has many local minima, which often results in suboptimal performances. In this paper we propose to use a global optimization technique known as continuation to alleviate this problem. Compared to other algorithms minimizing the same objective function, our continuation method often leads to lower test errors.

Author(s): Chapelle, O. and Chi, M. and Zien, A.
Book Title: ICML 2006
Journal: Proceedings of the 23rd International Conference on Machine Learning (ICML 2006)
Pages: 185-192
Year: 2006
Month: June
Day: 0
Editors: Cohen, W. W., A. Moore
Publisher: ACM Press

Department(s): Empirical Inference
Bibtex Type: Conference Paper (inproceedings)

DOI: 10.1145/1143844.1143868
Event Name: 23rd International Conference on Machine Learning
Event Place: Pittsburgh, PA., USA

Address: New York, NY, USA
Digital: 0
Language: en
Organization: Max-Planck-Gesellschaft
School: Biologische Kybernetik

Links: PDF


  title = {A Continuation Method for Semi-Supervised SVMs},
  author = {Chapelle, O. and Chi, M. and Zien, A.},
  journal = {Proceedings of the 23rd International Conference on Machine Learning (ICML 2006)},
  booktitle = {ICML 2006},
  pages = {185-192},
  editors = {Cohen, W. W., A. Moore},
  publisher = {ACM Press},
  organization = {Max-Planck-Gesellschaft},
  school = {Biologische Kybernetik},
  address = {New York, NY, USA},
  month = jun,
  year = {2006},
  month_numeric = {6}