Support Vector Machines

The advent of computers brought on rapid advances in the field of statistical classification, one of which is the Support Vector Machine, or SVM. The goal of an SVM is to take groups of observations and construct boundaries to predict which group future observations belong to based on their measurements. The different groups that must be separated will be called “classes”. SVMs can handle any number of classes, as well as observations of any dimension. SVMs can take almost any shape (including linear, radial, and polynomial, among others), and are generally flexible enough to be used in almost any classification endeavor that the user chooses to undertake. This new tutorial will introduce you to this supervised classification technique.