Introduction

Most research has shown that Support Vector Machines (SVM) and Neural Networks (NN) are powerful classification tools, which can be applied to several different areas. Unlike tree-based or probabilistic-based methods that were mentioned in the previous chapter, the process of how support vector machines and neural networks transform from input to output is less clear and can be hard to interpret. As a result, both support vector machines and neural networks are referred to as black box methods.

The development of a neural network is inspired by human brain activities. As such, this type of network is a computational model that mimics the pattern of the human mind. In contrast to this, support vector machines first map input data into a high dimension feature space defined by the kernel function and then find the optimum hyperplane that separates the training data by the maximum margin. In short, we can think of support vector machines as a linear algorithm in a high dimensional...