Lecture
Kernel Methods: Representer Theorem and SVM Applications
Description
This lecture covers the Representer Theorem, Regularized Empirical Risk Minimization in a Reproducing Kernel Hilbert Space (RKHS), Nonlinear Support Vector Machines (SVM), smoothness measurement in RKHS, kernel combinations, scalability issues for kernel methods, and the difference between convolution kernels and Mercer kernels.
Official source
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