Chapter 7

Support Vector Machines

Po-Wei Wang

National Taiwan UniversityTaipei, Taiwan

Chih-Jen Lin

National Taiwan UniversityTaipei, Taiwan

7.1 Introduction

Machine learning algorithms have a tendency to over-fit. It is possible to achieve an arbitrarily low training error with some complex models, but the testing error may be high, because of poor generalization to unseen test instances. This is problematic, because the goal of classification is not to obtain good accuracy on known training data, but to predict unseen test instances correctly. Vapnik’s work [34] was motivated by this issue. His work started from a statistical derivation on linearly separable scenarios, and found that classifiers ...

Get Data Classification now with the O’Reilly learning platform.

O’Reilly members experience books, live events, courses curated by job role, and more from O’Reilly and nearly 200 top publishers.