Radial Basis Function. Radial basis function is derived from…

Radial basis function is derived from Cover's Theorem of Separability of Patterns: "A complex pattern classification problem, cast in a high dimensional space non linearly is more likely to be linearly separable then in a low dimensional space provided that the space is not densely populated". Hidden neuron activations in RBFN are computed using an exponential of a distance measure (Euclidean distance) between input vectors and prototype vectors associated with hidden neurons. RBFN was originally introduced for the purpose of interpolation of data points on a finite training set T {Xk, dk } k 1 Q . Then solving the exact interpolation problem, we have to search for a map "f" such that f(x) dk k 1 to Q. There are 3 functions for RBF used in ML: 1. Gaussian functions 2. Multiquadric 3. Inverse multiquadric When deciding whether to use an RBF network or an MLP, there are several factors to consider.