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NeuroCOLT |
Neural Networks and Computational Learning Theory |
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NeuroCOLT Technical Report NC-TR-02-129
Support Vector
Machines are a family of algorithms for the analysis of data based
on convex Quadratic Programming. We focus on their use for classification,
where the SVM algorithms work by maximizing the margin of a classifying
hyperplane in a feature space. In this paper, based on a variation
of Random Sampling Techniques, techniques successfully used for similar
problems, we derive a randomized algorithm for training SVMs and formally
prove an upper bound on the expected running time
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