Efficient Classification for Additive Kernel SVMs
We show that a class of nonlinear kernel SVMs admits approximate classifiers with runtime and memory complexity that is independent of the number of support vectors. This class of kernels, which we refer to as additive kernels, includes widely used kernels for histogram-based image comparison like i...
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Veröffentlicht in: | IEEE transactions on pattern analysis and machine intelligence 2013-01, Vol.35 (1), p.66-77 |
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Sprache: | eng |
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