An alternative to SVM Method for Data Classification
Lakhdar Remaki
Support vector machine (SVM), is a popular kernel method for data
classification that demonstrated its efficiency for a large range of practical
applications. The method suffers, however, from some weaknesses including; time
processing, risk of failure of the optimization process for high dimension
cases, generalization to multi-classes, unbalanced classes, and dynamic
classification. In this paper an alternative method is proposed having a
similar performance, with a sensitive improvement of the aforementioned
shortcomings. The new method is based on a minimum distance to optimal
subspaces containing the mapped original classes.