Support Vector Machines: A Large Scale QP Problem
In this paper a new type of learning machines, named Support Vector Machines (SVMs), are discussed. In essence, given a training set — i.e., a number of previously classified patterns —, SVMs perform effective pattern recognition on a set of previously unseen patterns. We first review the theory of SCMs and some of their mathematical properties in detail. Then, we describe a few methods for the implementation of SVMs, which in the general case of large training sets requires the solution of large scale Quadratic Programming (QP) problem. Finally, we report the experimental results of the application of SCMs for the solution of a computer vision problem, appearance based 3-D object recognition from single image.
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