Machine Learning

Chapter 8

Support Vector Machine

8.1 Introduction

In machine learning, support vector machines (SVMs; also, support vector networks [1]) are supervised learning models with associated learning algorithms that analyze data and recognize patterns used for classification and regression analysis. Given a set of training examples, each marked for belonging to one of two categories, an SVM training algorithm builds a model that assigns new examples into one category or the other, making it a nonprobabilistic binary linear classifier. An SVM model is a representation of the examples as points in space, mapped, so that the examples of the separate categories are divided by a clear gap that is as wide as possible. New examples are then mapped into that ...