Bengio, Y. and Le Cun, Y. (2007). Scaling learning algorithms towards AI. In Large Scale Kernel Machines, edited by Bottou, L., Chapelle, O., De Coste, D., and Weston, J., MIT Press, Cambridge.
Cho, Y. and Saul, S. K. (2009). Kernel methods for deep learning. Advances in Neural Information Processing Systems, 22, 342-350.
Hwang, C. (2014). Support vector quantile regression for autoregressive data. Journal of the Korean Data & Information Science Society, 25, 1539-1547.
Hwang, C. (2015). Partially linear support vector orthogonal quantile regression with measurement errors. Journal of the Korean Data & Information Science Society, 26, 209-216.
Hwang, C. (2016). Multioutput LS-SVR based residual MCUSUM control chart for autocorrelated process. Journal of the Korean Data & Information Science Society, 27, 523-530.
Li, D., Tian, Y. and Xu, H. (2014). Deep twin support vector machine. In Proceedings of IEEE International Conference on Data Mining Workshop, 65-73, IEEE, Shenzhen, China.
Mercer, J. (1909). Functions of positive and negative type and their connection with theory of integral equations. Philosophical Transactions of Royal Society A, 209, 415-446.
Rumelhart, D. E., Hinton, G. E. and Williams, R. J. (1986). Learning internal representations by error propagation. Nature, 323, 533-536.
Seok, K. (2015). Semisupervised support vector quantile regression. Journal of the Korean Data & Information Science Society, 26, 517-524.
Shim, J. and Seok, K. (2014). A transductive least squares support vector machine with the difference convex algorithm. Journal of the Korean Data & Information Science Society, 25, 455-464.
Suykens, J. A. K. and Vanderwalle, J. (1999). Least square support vector machine classifier. Neural Pro-cessing Letters, 9, 293-300.
Suykens, J. A. K., Vandewalle, J. and DeMoor, B. (2001). Optimal control by least squares support vector machines. Neural Networks, 14, 23-35.
Vapnik, V. N. (1995). The nature of statistical learning theory, Springer, New York.
Wahba, G. (1990). Spline models for observational data, CMMS-NSF Regional Conference Series in Applied Mathematics, 59, SIAM, Philadelphia.
Wiering, M. A. and Schomaker, L. R. B. (2014). Multi-layer support vector machines. In Regularization, Optimization, Kernels, and Support Vector Machines, edited by Suykens, Signoretto and Argyriou, Chapman & Hall/CRC, Boca Raton.
Zhuang, Z., Tsang, I. W. and Choi, S. C. H. (2011). Two-layer multiple kernel learning. In Proceedings of International Conference on Artificial Intelligence and Statistics, 909-917.