Kernel Methods: Representer Theorem and SVM Applications

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.