Support Vector Machines (SVM)
Shriram K. Vasudevan, Nitin Vamsi Dantu, Sini Raj Pulari, T. S. Murugesh · 2023
Chapter 7 details the classification algorithm support vector machines (SVM) which are known to offer greater accuracy. The utmost aim of the SVM algorithm, to create an optimum line or a decision boundary to segregate the dataset into classes, is emphasized. The appropriate decision boundary, referred to as a hyperplane, is presented in a detailed manner, together with the terminologies used in SVM. The method by which SVM works is also included with the oneAPI implementation of SVM, with step-by-step screenshots presented with the results validating its effectiveness. The codes are made available in the GitHub links for easier and quicker reference. A quiz and key points are also provided at the end of this chapter.