Bazaraa MS, Sherali HD, Shetty CM. (1993). Nonlinear programming theory and algorithms (2nd ed).

Bennett KP, Momma M. (2002). A pattern search method for model selection of support vector regression Proceedings of SIAM Conference on Data Mining.

Bonnans JF, Shapiro A. (1998). Optimization problems with perturbations: A guided tour SIAM Rev. 40

Brown M, Gunn SR, Gao JB, Harris CJ. (2002). A probabilistic framework for SVM regression and error bar estimation Mach Learn. 46

Chang CC, Lin CJ. (2001). LIBSVM: A library for Support Vector Machines Available online at: http:--www.csie.ntu.edu.tw-cjlin-libsvm.

Chang CC, Lin CJ. (2002). Training nu-support vector regression: theory and algorithms. Neural computation. 14 [PubMed]

Chu W, Keerthi SS, Ong CJ. (2004). Bayesian support vector regression using a unified loss function. IEEE transactions on neural networks. 15 [PubMed]

Chung KM, Kao WC, Sun CL, Wang LL, Lin CJ. (2003). Radius margin bounds for support vector machines with the RBF kernel. Neural computation. 15 [PubMed]

Clarke FH. (1983). Optimization and nonsmooth analysis.

Golub GH, van_Loan CF. (1996). Matrix computations.

Joachims T. (2000). Estimating the generalization performance of a SVM efficiently Proceedings of the International Conference on Machine Learning.

Kwok JT. (2001). Linear dependency between epsilon and the input noise in epsilon-support vector regression Proceedings of the International Conference on Artificial Neural Networks ICANN.

Lin CJ. (2001). Formulations of support vector machines: A note from an optimization point of view Neural Comput. 13

Lin CJ. (2001). On the convergence of the decomposition method for support vector machines. IEEE transactions on neural networks. 12 [PubMed]

Lin CJ, Weng RC. (2004). Simple probabilistic predictions for support vector regression Tech Rep.

Scholkopf B, Smola A. (2004). A tutorial on support vector regression Statistics Of Computing. 14

Scholkopf B, Smola A, Muller KR, Murata N. (1998). Asymptotically optimal choice of epsilon-loss for support vector machines Proceedings of the International Conference on Artificial Neural Network.

Ulbrich M. (2000). Nonsmooth Newton-like methods for variational inequalities and constrained optimization problems in function Spaces Unpublished doctoral dissertation.

Vapnik V. (1998). Statistical Learning Theory.

Vapnik V, Chapelle O. (2000). Bounds on error expectation for support vector machines. Neural computation. 12 [PubMed]

Vapnik V, Cortes C. (1995). Support-vector networks Mach Learn. 20

Vapnik V, Guyon I, Boser BE. (1992). A training algorithm for optimal margin classifiers Proceedings Of The Fifth Annual Workshop Of Computational Learning Theory. 5

Vapnik V, Mukherjee S, Chapelle O, Bousquet O. (2002). Choosing multiple parameters for support vector machines Machine Learning. 46

Gunter L, Zhu J. (2007). Efficient computation and model selection for the support vector regression. Neural computation. 19 [PubMed]