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About this paper
Ivan Silajev
The authors show that dual objectives for support vector classification and regression are constrained minimizations of KL divergence between Gaussian measures. For classification, the divergence reproduces the dual up to a term independent of its variable; auxiliary terms encode regression’s L1 penalty and kernel ridge regression’s L2 penalty. This is an objective-level equivalence, not evidence of improved prediction or efficiency.
Summary by alphaXiv.
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