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Support vector machine (SVM), is a popular kernel method for dataclassification that demonstrated its efficiency for a large range of practicalapplications. The method suffers, however, from some weaknesses including; timeprocessing, risk of failure of the optimization process for high dimensioncases, generalization to multi-classes, unbalanced classes, and dynamicclassification. In this paper an alternative method is proposed having asimilar performance, with a sensitive improvement of the aforementionedshortcomings. The new method is based on a minimum distance to optimalsubspaces containing the mapped original classes.

An alternative to SVM Method for Data Classification