Stochastic functional descent for learning Support Vector Machines
We present a novel method for learning Support Vector Machines (SVMs) in the online setting. Our method is generally applicable in that it handles the online learning of the binary, multiclass, and structural SVMs in a unified view. The SVM learning problem consists of optimizing a convex object...
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| Language: | en_US |
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2016
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| Online Access: | https://hdl.handle.net/2144/14104 |