Theory, Implementation, and Applications of Support Vector Machines
Support Vector Machines (SVMs) have been recently introduced as a new method for function estimation. In this survey we first review the main theoretical properties of SVMs, then present an implementation of SVMs able to work with training sets of very large size. Finally, we discuss two computer vision applications in which SVMs for both pattern recognition and regression estimation have been successfully employed.
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