References
- Breiman, L. (1996). Bagging predictors. Machine Learning, 24, 123-140.
- Espinoza, M., Suykens, J.A.K. and De Moor, B. (2005). Load forecasting using least squares Support vector machines. Lecture Notes in Computer Science, 3512, 1018-1026.
- Girolami, M. (2003). Orthogonal series density estimation and kernel eigenvalue problem. Neural Computation, 14, 669-688.
- Kimeldorf, G. S. and Wahba, G. (1971). Some results on Tchebycheffian spline functions. Journal of Mathematical Analysis and its Applications, 33, 82-95. https://doi.org/10.1016/0022-247X(71)90184-3
- Mercer, J. (1909). Functions of positive and negative type and their connection with theory of integral equations. Philosophical Transactions of Royal Society of London A, 415-446.
- Scholkopf, B., Burges, C. and Vapnik, V. (1995). Extracting support data for a given task. In Proceedings of First Conference on Knowledge Discovery and Data Mining , 252-257, Menlo Park, CA.
- Seok, K. H. (2014). Semi-supervised classification with LS-SVM formulation. Journal of the Korean Data & Information Science Society, 21, 461-470.
- Shim, J. and Hwang, C. (2013). Expected shortfall estimation using kernel machines. Journal of the Korean Data & Information Science Society, 24, 625-636. https://doi.org/10.7465/jkdi.2013.24.3.625
- Suykens, J. A. K. and Vanderwalle, J. (1999). Least square support vector machine classifier. Neural Processing Letters, 9, 293-300. https://doi.org/10.1023/A:1018628609742
- Suykens, J. A. K. and Vandewalle, J. (1999). Multiclass least squares support vector machines. In Proceeding of the International Joint Conference on Neural Networks,, 900-903, Washington DC.
- Vapnik, V. N. (1995). The nature of statistical learning theory, Springer, New York.
- Vapnik, V. N. (1998). Statistical learning theory, Springer, New York.
- Weston, J. and Watkins, C. (1998). Multi-class SVM, Technical Report 98-04, Royal Holloway University, London.
- Williams, C. K. I. and Seeger, M. (2001). Using the Nystrom method to speed up kernel machines. In Proceeding of Neural Information Processing Systems Conference 13, 682-699, MIT press.
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- LS-SVM for large data sets vol.27, pp.2, 2016, https://doi.org/10.7465/jkdi.2016.27.2.549