Random Features for Large-Scale Kernel Machines
The most popular methods for large-scale kernel machines are decomposition methods for solving Support Vector Machines (SVM). These methods iteratively update a subset of the kernel machine’s coefficients using coordinate ascent until KKT conditions are satisfied to within a tolerance [5, 6].
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