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Optimization Techniques for Semi-Supervised Support Vector Machines

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http://pubman.mpdl.mpg.de/cone/persons/resource/persons83855

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

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Chapelle, O., Sindhwani, V., & Keerthi, S. (2008). Optimization Techniques for Semi-Supervised Support Vector Machines. Journal of Machine Learning Research, 9, 203-233. Retrieved from http://jmlr.csail.mit.edu/papers/volume9/chapelle08a/chapelle08a.pdf.


Cite as: http://hdl.handle.net/11858/00-001M-0000-0013-CA6D-1
Abstract
Due to its wide applicability, the problem of semi-supervised classification is attracting increasing attention in machine learning. Semi-Supervised Support Vector Machines (S3VMs) are based on applying the margin maximization principle to both labeled and unlabeled examples. Unlike SVMs, their formulation leads to a non-convex optimization problem. A suite of algorithms have recently been proposed for solving S3VMs. This paper reviews key ideas in this literature. The performance and behavior of various S3VMs algorithms is studied together, under a common experimental setting.