A Continuation Method for Semi-supervised SVMs. Chapelle, O., Chi, M., & Zien, A. In Proceedings of the International Conference on Machine Learning, pages 185--192, 2006.
abstract   bibtex   
Semi-Supervised Support Vector Machines (S 3 VMs) 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.
@InProceedings{Chapelle2006,
  Title                    = {A Continuation Method for Semi-supervised SVMs},
  Author                   = {Chapelle, O. and Chi, M. and Zien, A.},
  Booktitle                = {Proceedings of the International Conference on Machine Learning},
  Year                     = {2006},
  Pages                    = {185--192},

  Abstract                 = {Semi-Supervised Support Vector Machines
(S
3
VMs) 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.},
  Timestamp                = {2014.10.24}
}
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