Scaling-up support vector machines using boosting algorithm

In the recent years support vector machines (SVM) have been successfully applied to solve a large number of classification problems. Training an SVM, usually posed as a quadratic programming (QP) problem, often becomes a challenging task for the large data sets due to the high memory requirements and slow convergence. We propose to apply boosting to Platt´s sequential minimal optimization (SMO) algorithm (1999) and to use resulting Boost-SMO method for speeding and scaling up the SVM training. Experiments on three commonly used benchmark data sets show that Boost-SMO achieves classification accuracy comparable to conventional SMO but is a factor of 3 to 10 faster. The speed-up could easily be orders of magnitude on the larger data sets