Machine Learning with R - Third Edition

Understanding support vector machines

A support vector machine (SVM) can be imagined as a surface that creates a boundary between points of data plotted in a multidimensional space representing examples and their feature values. The goal of an 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, 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.