1. Thornton, C., Hutter, F., Hoos, H. H. and Leyton-Brown, K. 2013. Auto-WEKA: combined selection and hyperparameter optimization of classification algorithms. In Proceedings of the Proceedings of the 19th ACM SIGKDD international conference on Knowledge discovery and data mining (Chicago, Illinois, USA, 2013). ACM, Chicago, 847--855. DOI=10.1145/2487575.2487629.
2. Ortiz-García, E. G., Salcedo-Sanz, S., Pérez-Bellido, Á. M. and Portilla-Figueras, J. A. 2009. Improving the training time of support vector regression algorithms through novel hyper-parameters search space reductions. Neurocomputing. 72, 16, (October, 2009), 3683--3691. DOI=https://doi.org/10.1016/j.neucom.2009.07.009.
3. Adankon, M. M. and Cheriet, M. 2009. Model selection for the LS-SVM. Application to handwriting recognition. Pattern. Recogn. 42, 12 (December 2009), 3264--3270. DOI=https://doi.org/10.1016/j.patcog.2008.10.023.
4. Rivas-Perea, P., Cota-Ruiz, J., Rosiles, J.-G. J. 2014. A nonlinear least squares quasi-Newton strategy for LP-SVR hyper-parameters selection. International Journal of Machine Learning and Cybernetics, 5, 4 (August, 2014), 579--597. DOI=10.1007/s13042-013-0153-9.
5. Liu, J., Gan, X. S. and Gao, W. M. 2014. Hyper-Parameters Selection of LS-SVM Based on PSO Algorithm with Multi-Particles Sharing Strategy. Adv. Mat. Res. 1049--1050 (2014), 1654--1657. DOI=10.4028/www.scientific.net/AMR.1049--1050.1654.