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Empirical Choice of the Shape Parameter for Robust Support Vector Machines

  • Pak, Ro-Jin (Department of Information & Statistics, Dankook University)
  • Published : 2008.07.16

Abstract

Inspired by using a robust loss function in the support vector machine regression to control training error and the idea of robust template matching with M-estimator, Chen (2004) applies M-estimator techniques to gaussian radial basis functions and form a new class of robust kernels for the support vector machines. We are specially interested in the shape of the Huber's M-estimator in this context and propose a way to find the shape parameter of the Huber's M-estimating function. For simplicity, only the two-class classification problem is considered.

Keywords

References

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