Communications for Statistical Applications and Methods

The Korean Statistical Society (한국통계학회)
권호 표지
DOI QR코드

DOI QR Code

Empirical Choice of the Shape Parameter for Robust Support Vector Machines

  • Pak, Ro-Jin (Department of Information & Statistics, Dankook University)
  • Published : 2008.07.16
Download PDF
⟨ Previous Next ⟩

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

  1. Chen, J. (2004). M-estimation based robust kernels for support vector machines, In Proceeding of the 17th International Conference on Pattern Recognition, 168-171
  2. Huber, P. J. (1981). Robust Statistics, John Wiley & Sons, New York
  3. Kwok, J. T. Y. (2000). The evidence framework applied to support vector machines, IEEE Transactions on Neural Networks, 11, 1162-1173 https://doi.org/10.1109/72.870047
  4. 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 https://doi.org/10.1109/34.877518
  5. Scholkopf, B. and Smola, A. J. (2002). Learning with Kernels-Support Vector Machines, Regularization, Optimization and Beyond, The MIT Press, Cambridge