Non-symmetric Support Vector Machines

Authors:
Jianfeng Feng
Affiliations:
-
Venue:
IWANN '01 Proceedings of the 6th International Work-Conference on Artificial and Natural Neural Networks: Connectionist Models of Neurons, Learning Processes and Artificial Intelligence-Part I
Year:
2001

Citing 2
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An introduction to support Vector Machines: and other kernel-based learning methods

An introduction to support Vector Machines: and other kernel-based learning methods
Bounds on Error Expectation for Support Vector Machines

Neural Computation

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Abstract

A novel approach to calculate the generalization error of the support vector machines and a new support vector machine-non-symmatic support vector machine-is proposedhere. Our results are based upon the extreme value theory and both the mean and variance of the generalization error are exactly ontained.