Simple Learning Algorithms for Training Support Vector Machines

Support Vector Machines SVMs have proven to be highly e ective for learning many real world datasets but have failed to establish them selves as common machine learning tools This is partly due to the fact that they are not easy to implement and their standard imple mentation requires the use of optimization packages In this paper we present simple iterative algorithms for training support vector ma chines which are easy to implement and guaranteed to converge to the optimal solution Furthermore we provide a technique for automati cally nding the kernel parameter and best learning rate Extensive experiments with real datasets are provided showing that these al gorithms compare well with standard implementations of SVMs in terms of generalisation accuracy and computational cost while being signi cantly simpler to implement