Journal of the Korean Data and Information Science Society

The Korean Data and Information Science Society (한국데이터정보과학회)
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Sparse kernel classication using IRWLS procedure

  • Kim, Dae-Hak (School of Computer & Information Communication Engineering, Catholic University of Daegu)
  • Published : 2009.07.31
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Abstract

Support vector classification (SVC) provides more complete description of the lin-ear and nonlinear relationships between input vectors and classifiers. In this paper. we propose the sparse kernel classifier to solve the optimization problem of classification with a modified hinge loss function and absolute loss function, which provides the efficient computation and the sparsity. We also introduce the generalized cross validation function to select the hyper-parameters which affects the classification performance of the proposed method. Experimental results are then presented which illustrate the performance of the proposed procedure for classification.

Keywords

References

  1. Craven, P. and Wahba, G. (1979). Smoothing noisy data with spline functions: Estimating the correct degree of smoothing by the method of generalized cross-validation. Numerishe Mathematik, 31, 377-390.
  2. Gunn, S. (1998). Support vector machines for classiflcation and regression. ISIS Technical Report, University of Southhampton.
  3. Mercer, J. (1909). Functions of positive and negative type and their connection with theory of integral equations. Philosophical Transactions of Royal Society, Series A, 209, 415-446. https://doi.org/10.1098/rsta.1909.0016
  4. Oh, K., Shim, J. and Kim, D. (2003). Incremental multi-classification by least squares support vector machine. Journal of Korean Data & Information Science Society, 14, 965-974.
  5. Perez-Cruz, F., Navia-Vazquez, A., Alarcon-Diana, P. L. and Artes-Rodriguez, A. (2000). An IRWLS procedure for SVR. In Proceedings of European Association for Signal Processing, EUSIPO 2000, Tampere, Finland.
  6. Platt, J. (1998). Sequential minimal optimization: A fast algorithm for training support vector machines. Microsoft Research Technical Report, MSR-TR-98-14.
  7. Seok, K. H. (2007). Semi-supervised learning using kernel estimation. Journal of Korean Data & Information Science Society, 18, 629-636.
  8. Smola, A. and Scholkopf, B. (1998). On a kernel-based method for pattern recognition, regression, approximation and operator inversion. Algorithmica, 22, 211-231. https://doi.org/10.1007/PL00013831
  9. Tipping, M. E. (2001). Sparse Bayesian learning and the relevance vector machine. Journal of Machine Learning Research, 1, 211-244. https://doi.org/10.1162/15324430152748236
  10. Vapnik, V. N. (1995). The nature of statistical learning theory, Springer, New York.
  11. Vapnik, V. N. (1998). Statistical learning theory, John Wiley, New York.
  12. Williams, P. M. (1995). Bayesian regularization and pruning using a laplace prior. Neural Computation, 7, 117-143. https://doi.org/10.1162/neco.1995.7.1.117