An experimental comparison of some efficient approaches for training support vector machinesAn experimental comparison of some efficient approaches for training support vector machines Peer-Reviewed Journal Publication Δημοσίευση σε Περιοδικό με Κριτές 2015-11-182004enSupport Vector Machines (SVMs) are one of the most widely used techniques for developing classification and regression models. A significant portion of the recent research on SVMs is devoted to the development of efficient computational approaches for SVM training. This paper performs an experimental analysis of some approaches recently developed for training SVM classification models, including decomposition algorithms, explicit solution techniques, and linear programming. The analysis involves the generalizing performance of the SVM models and the computational efficiency of the algorithms. The results lead to useful conclusions on the performance of the training techniques and to the applicability of linear and non-linear SVM models.http://creativecommons.org/licenses/by/4.0/Operational Research, An International Journal4145-56 Michael Doumpos Δουμπος Μιχαλης Springer Verlag Classification Support vector machines Linear programming Experimental analysis