A Support Vector Machine (SVM) can be imagined as a surface that creates a boundary between points of data plotted in multidimensional that represent examples and their feature values. The goal of a SVM is to create a flat boundary called a hyperplane, which divides the space to create fairly homogeneous partitions on either side. In this way, the SVM learning combines aspects of both the instance-based nearest neighbor learning presented in Chapter 3, Lazy Learning – Classification Using Nearest Neighbors, and the linear regression modeling described in Chapter 6, Forecasting Numeric Data – Regression Methods. The combination is extremely powerful, allowing SVMs to model highly complex relationships.

Although the basic mathematics that drive SVMs have been around for decades, they have recently exploded in popularity. This is, of course, rooted in their state-of-the-art performance, but perhaps also due to the fact that award winning SVM algorithms...