The Support Vector Machine (SVM) is a supervised algorithm for the
solution of classification and regression problems. SVMs have gained
widespread use in recent years because of successful applications like
character recognition and the profound theoretical underpinnings concerning
generalization performance. Yet, one of the remaining drawbacks
of the SVM algorithm is its high computational demands during
the training and testing phase. This article describes how to efficiently
parallelize SVM training in order to cut down execution times. The parallelization
technique employed is based on a decomposition approach,
where the inner quadratic program (QP) is solved using Sequential Minimal
Optimization (SMO). Thus all types of SVM formulations can be
solved in parallel, including C-SVC and nu-SVC for classification as well
as epsilon-SVR and nu-SVR for regression. Practical results show, that on most
problems linear or even superlinear speedups can be attained
