Table of content:
Introduction, Cover‟s Theorem on the separability of Patterns, The Interpolation problem, Radial-Basis-Function Networks, K-Means Clustering, Recursive Least-Squares Estimation of the Weight Vector, Hybrid Learning Procedure for RBF Networks, Kernel Regression and Its Relation to RBF Networks
Kernels or kernel methods(also called Kernel functions)are sets of different types of algorithms that are being used for pattern analysis. They are used to solve a non-linear problem by using a linear classifier.
Sometimes it may be difficult to divide lower dimension data set using a linear line or hyperplane. Here comes the use of kernel function which takes the points to higher dimensions. This solves the problem over there and returns the output.
Consider the situation shown in the figure given in next slide.
There are different types of kernel like linear kernel, polynomial kernel, exponential kernel, Gaussian kernel, etc.
Gaussian kernel and exponential kernel are examples of a radial basis function kernel. The equations given below are Gaussian and exponential kernel functions respectively.