PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

Branch and Bound for Semi-Supervised Support Vector Machines
Olivier Chapelle, Vikas Sindhwani and Sathiya Keerthi
In: NIPS(2007).

Abstract

Semi-supervised SVMs (S3VM) attempt to learn low-density separators by maximizing the margin over labeled and unlabeled examples. The associated optimization problem is non-convex. To examine the full potential of S3VMs modulo local minima problems in current implementations, we apply branch and bound techniques for obtaining exact, global ly optimal solutions. Empirical evidence suggests that the globally optimal solution can return excellent generalization performance in situations where other implementations fail completely. While our current implementation is only applicable to small datasets, we discuss variants that can potentially lead to practically useful algorithms.

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EPrint Type:Conference or Workshop Item (Paper)
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Learning/Statistics & Optimisation
Theory & Algorithms
ID Code:2627
Deposited By:Olivier Chapelle
Deposited On:22 November 2006