My education in the fundamentals of machine learning has mainly come from Andrew Ng’s excellent Coursera course on the topic. One thing that wasn’t covered in that course, though, was the topic of “boosting” which I’ve come across in a number of different contexts now. Fortunately, it’s a relatively straightforward topic if you’re already familiar with machine learning classification.
The final step of the HOG detection process is to cluster the search results.
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.