This chapter introduces kernel functions, binary support vectors classifiers, one-class support vector machines for anomaly detection, and support vector regression.

In the Binomial classification section in Chapter 6, Regression and Regularization, you learned the concept of hyperplanes to segregate observations from the training set and estimate the linear decision boundary. The logistic regression has at least one limitation: it requires that the datasets be linearly separated using a defined function (sigmoid). This limitation is especially an issue for high-dimension problems (large number of features that are highly nonlinearly dependent). Support vector machines (SVMs) overcome this limitation by estimating the optimal separating hyperplane using kernel functions.

In this chapter, we will cover the following topics: