I. Introduction
Support Vector Machine (SVMs) were developed to solve the classification problem, but, recently, have been applied to solve regression ones [2]. The classifiers generated by a Support Vector Machine achieve good results in general, having that capacity of generalization measured by their efficiency on classifying data that does not belong to the training data set. The foundation of Support Vector Machines (SVM) was developed by Vapnik [1] and earned a lot of popularity due its promising characteristics, with better empirical performance. The mathematical formulation uses the Structural Risk Minimization (SRM), that has shown itself superior to the Empirical Risk Minimization (ERM), used by conventional Neural Nets. SRM minimizes an upper limit over the expected risk, while ERM minimizes the error on the training data. This is the difference that leads SVM to have greater generalization capacity, which is the goal of statistical learning.