SVM (support vector machines) techniques have recently arrived to complete the wide range of classification methods for complex systems. These classification systems offer similar performances to other classifiers (such as the neuronal networks or classic statistical classifiers) and they are becoming a valuable tool in industry for the resolution of real problems. One of the fundamental elements of this type of classifier is the metric used for determining the distance between samples of the population to be classified. Although the Euclidean distance measure is the most natural metric for solving problems, it presents certain disadvantages when trying to develop classification systems that can be adapted as the characteristics of the sample space change. Our study proposes a means of avoiding this problem using the multivariate normalization of the inputs (both during the training and classification processes). Using experimental results produced from a significant number of populations, the study confirms the improvement achieved in the classification processes. Lastly, the study demonstrates that the multivariate normalization applied to a real SVM is equivalent to the use of a SVM that uses the Mahalanobis distance measure, for non-normalized data. 
Training of support vector machine with the use of multivariate normalization