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Empirical Choice of the Shape Parameter for Robust Support Vector Machines
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 Title & Authors
Empirical Choice of the Shape Parameter for Robust Support Vector Machines
Pak, Ro-Jin;
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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.
Huber's M-estimator;support vector machine;two-class classification;
 Cited by
Chen, J. (2004). M-estimation based robust kernels for support vector machines, In Proceeding of the 17th International Conference on Pattern Recognition, 168-171

Huber, P. J. (1981). Robust Statistics, John Wiley & Sons, New York

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Mangasarian, O. L. and Musicant, D. R. (2000). Robust linear and support vector regression. IEEE Transaction on Pattern Analysis and Machine Intelligence, 22, 950-955 crossref(new window)

Scholkopf, B. and Smola, A. J. (2002). Learning with Kernels-Support Vector Machines, Regularization, Optimization and Beyond, The MIT Press, Cambridge