Kernel Methods: Nonlinear Separation Surfaces

This lecture covers kernel methods for designing nonlinear separation surfaces. It explains how linear classifiers like logistic regression and Perceptron can be adapted using kernel methods to handle data that is not linearly separable. The lecture introduces polynomial and Gaussian kernels, illustrating how nonlinear decision boundaries can be achieved by transforming feature vectors into higher dimensions. It also discusses the kernel-based Perceptron algorithm, both unregularized and regularized versions, and demonstrates how kernel-based SVM classifiers can be used to determine nonlinear separation curves.