RBF Network MATLAB Code
UPDATE 8/26: There is now example code for both classification and function approximation.
UPDATE 8/26: There is now example code for both classification and function approximation.
A Radial Basis Function Network (RBFN) is a particular type of neural network. In this article, I’ll be describing it’s use as a non-linear classifier.
Each RBF neuron computes a measure of the similarity between the input and its prototype vector (taken from the training set). Input vectors which are more similar to the prototype return a result closer to 1. There are different possible choices of similarity functions, but the most popular is based on the Gaussian. Below is the equation for a Gaussian with a one-dimensional input.
In order to build an effective machine learning solution, you will need the proper analytical tools for evaluating the performance of your system. Cross-validation is one of the most important tools, as it gives you an honest assessment of the true accuracy of your system.
To help in my understanding of the HOG descriptor, as well as to allow me to easily test out modifications to the descriptor, I wrote functions in Octave / Matlab for computing the HOG descriptor for a detection window.