PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

A Continuation Method for Semi-Supervised SVMs
Olivier Chapelle, Mingmin Chi and Alexander Zien
In: ICML(2006).

Abstract

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.

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EPrint Type:Conference or Workshop Item (Paper)
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Learning/Statistics & Optimisation
Theory & Algorithms
ID Code:2629
Deposited By:Olivier Chapelle
Deposited On:22 November 2006