Abstract

Support Vector Machines (SVMs) have proven to be highly effective for learning many real world datasets but have failed to establish themselves as common machine learning tools. This is partly due to the fact that they are not easy to implement, and their standard implementation requires the use of optimization packages. In this paper we present simple iterative algorithms for training support vector machines which are easy to implement and guaranteed to converge to the optimal solution. Furthermore we provide a technique for automatically finding the kernel parameter and best learning rate. Extensive experiments with real datasets are provided showing that these algorithms compare well with standard implementations of SVMs in terms of generalisation accuracy and computational cost, while being significantly simpler to implement. 1 Introduction Since their introduction by Vapnik and coworkers [38, 7], Support Vector Machines (SVMs) have been successfully applied to a number of real ...

Citations