Escaping the Convex Hull with Extrapolated Vector Machines

نویسنده

  • Patrick Ha
چکیده

Extrapolated Vector Machines. Patrick Ha ner AT&T Labs-Research, 200 Laurel Ave, Middletown, NJ 07748 [email protected] Abstract Maximum margin classi ers such as Support Vector Machines (SVMs) critically depends upon the convex hulls of the training samples of each class, as they implicitly search for the minimum distance between the convex hulls. We propose Extrapolated Vector Machines (XVMs) which rely on extrapolations outside these convex hulls. XVMs improve SVM generalization very signi cantly on the MNIST [7] OCR data. They share similarities with the Fisher discriminant: maximize the inter-class margin while minimizing the intra-class disparity.

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تاریخ انتشار 2001