Algorithmic Approaches to Training Support Vector Machines: A Survey
Colin K. Campbell · 2000
: Support Vector Machines (SVMs) have become an increasingly popular tool for machine learning tasks involving classification, regression or novelty detection. They exhibit good generalisation performance on many reallife datasets and the approach is well-motivated theoretically. Training involves optimisation of a convex cost function, there are relatively few free parameters to adjust and the architecture does not have to be found by experimentation. In this tutorial we survey methods for training SVMs including model selection strategies for determining the free parameters and new techniques for active selection of training examples. 1. Introduction. Support Vector Machines (SVMs) have recently been successfully applied to a number of applications ranging from particle identification, face identification and text categorisation to engine knock detection, bioinformatics and database marketing [9]. The approach is systematic and motivated by statistical learning theory [26] and Baye...