Radial Basis Function

Today I'm going to give a small introduction for a simple Machine Learning Algorithm called Radial Basis Function. Actually I was learnt this method in my machine learning class. You can get a good understand by referring my assignment. 
 
Neural Networks offer a powerful framework for representing nonlinear mappings from several inputs to one or more outputs. An important application of neural networks is regression. Instead of mapping the inputs into a discrete class label, the neural network maps the input variables into continuous values.

A major class of neural networks is the radial basis function (RBF) neural network. We will look at the architecture of RBF neural networks, followed by its applications in both regression and classification.

Main Features of RBF

They are two-layer feed-forward networks.

The hidden nodes implement a set of radial basis functions (e.g. Gaussian functions).

The output nodes implement linear summation functions as in an MLP.

The network training is divided into two stages: first the weights from the input to hidden layer are determined, and then the weights from the hidden to output layer.

The training/learning is very fast.

The networks are very good at interpolation

Please see the following links

My Assignment submission

My Radial Basis Function Project