Branch and Bound for Semi-Supervised Support Vector Machines

Chapelle, O; Sindhwani, V; Keerthi, SS

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Branch and Bound for Semi-Supervised Support Vector Machines

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Chapelle, O
Department Empirical Inference, Max Planck Institute for Biological Cybernetics, Max Planck Society;
Max Planck Institute for Biological Cybernetics, Max Planck Society;

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https://papers.nips.cc/paper/3135-branch-and-bound-for-semi-supervised-support-vector-machines.pdf
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Citation

Chapelle, O., Sindhwani, V., & Keerthi, S. (2007). Branch and Bound for Semi-Supervised Support Vector Machines. In B. Schölkopf, J. Platt, & T. Hoffman (Eds.), Advances in Neural Information Processing Systems 19 (pp. 217-224). Cambridge, MA, USA: MIT Press.

Cite as: https://hdl.handle.net/11858/00-001M-0000-0013-CBD7-7

Abstract

Semi-supervised SVMs (S3VMs) 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, globally 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.