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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.
