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Abstract:
Maximum margin classifiers 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 significantly on the
MNIST [7] OCR data. They share similarities with the Fisher
discriminant: maximize the inter-class margin while minimizing
the intra-class disparity.
References
[7] Y. LeCun, L. Bottou, Y. Bengio, and P. Haffner.
Gradient-based learning applied to document recognition.
Proceedings of the IEEE
, 86(11), 1998.
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