Comak, EmreArslan, Ahmet2020-03-262020-03-2620080957-4174https://dx.doi.org/10.1016/j.eswa.2007.08.047https://hdl.handle.net/20.500.12395/22168For training of support vector machines (SVMs) efficiently, a new training algorithm, clustering k-NN (k-nearest neighbor) support vector machines (CKSVMs) based on a Gaussian function regulated locally is proposed. In order to reflect degree of training data point as a support vector the Gaussian function is used with k-nearest neighbor (k-NN) method and Euclidean Distance measure. To add local control property to the training algorithm, a simple clustering scheme is implemented before Gaussian functions are constructed for each cluster. In addition, probabilistic SVM outputs are used for extension from binary classification to multi-class classification in pairwise approach. This training algorithm is applied to three commonly used classification problems. Experimental results show that the CKSVM has more classification accuracy than standard multi-class LS-SVM, FLS-SVM and LS-SVM with k-NN method which is proposed in our previous study. In addition to this, the training algorithm highly improved efficiency of the SVM classifier via simple algorithm. (c) 2007 Elsevier Ltd. All rights reserved.en10.1016/j.eswa.2007.08.047info:eu-repo/semantics/closedAccesssupport vector machinesleast squares support vector machinesGaussian functionsk-nearest neighborprobabilistic outputsReview353564568Q1WOS:000257993700001Q1