Support Vector Machines

In this chapter, we're going to introduce another approach to classification using a family of algorithms called support vector machines. They can work with both linear and non-linear scenarios, allowing high performance in many different contexts. Together with neural networks, SVMs probably represent the best choice for many tasks where it's not easy to find out a good separating hyperplane. For example, for a long time, SVMs were the best choice for MNIST dataset classification, thanks to the fact that they can capture very high non-linear dynamics using a mathematical trick, without complex modifications in the algorithm. In the first part, we're going to discuss the basics of linear SVM, which then will be used for their non-linear extensions. We'll also discuss some techniques to control the number of parameters and, at the end...