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7

ICML
2006
IEEE

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A continuation method for semi-supervised SVMs

14 years 5 months ago

A continuation method for semi-supervised SVMs

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Semi-Supervised Support Vector Machines (S3 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.

Olivier Chapelle, Mingmin Chi, Alexander Zien

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Continuation Method | ICML 2006 | Machine Learning | Objective Function Favors | Optimization Problem |

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Added	17 Nov 2009
Updated	17 Nov 2009
Type	Conference
Year	2006
Where	ICML
Authors	Olivier Chapelle, Mingmin Chi, Alexander Zien

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