1 |
Cho, D. H., Shim, J. and Seok, K. H. (2010). Doubly penalized kernel method for heteroscedastic autoregressive data. Journal of the Korean Data & Information Science Society, 21, 155-162.
|
2 |
Hwang, H. (2010). Fixed size LS-SVM for multiclassication problems of large datasets. Journal of the Korean Data & Information Science Society, 21, 561-567.
|
3 |
Kuhn, H. W. and Tucker, A. W. (1951). Nonlinear programming. Proceedings of 2nd Berkeley Symposium, 481-492.
|
4 |
Mercer, J. (1909). Functions of positive and negative type and their connection with theory of integral equations. Philosophical Transactions of Royal Society A, 415-446.
|
5 |
Nychka, D., Gray, G., Haaland, P., Martin, D. and O'Connell, M. (1995). A nonparametric approach syringe grading for quality improvement. Journal of American Statistical Association, 432, 1171-1178.
|
6 |
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.
|
7 |
Wang, L.(Ed.) (2005). Support vector machines: Theory and application, Springer, New York.
|
8 |
Platt, J. (1998). Sequential minimal optimization: A fast algorithm for training support vector machines, Technical Report MSR-TR-98-14, Microsoft Research, California.
|
9 |
Shim, J., Kim, C. and Hwang, C. (2011). Semiparametric least squares support vector machine for accelerated failure time model. Journal of the Korean Statistical Society, 40, 75-83.
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DOI
ScienceOn
|
10 |
Smola, A. J. and Scholkopf, B. (1998). On a kernel-based method for pattern recognition, regression, approximation and operator inversion. Algorithmica, 22, 211-231.
DOI
|
11 |
Vapnik, V. N. (1995). The nature of statistical learning theory, Springer, New York.
|
12 |
Vapnik, V. N. (1998). Statistical learning theory, John Wiley, New York.
|
13 |
Wahba, G., Lin, Y. and Zhang, H. (1999). Generalized approximate cross validation for support vector machines, or another way to look at margin-like quantities, Technical Report 1006, University of Wisconsin, Wisconsin.
|
14 |
Yuan, M. (2006). GACV for quantile smoothing splines. Computational Statistics and Data Analysis, 50, 813-829.
DOI
ScienceOn
|