the ‘Winner takes all’ strategy. A Kernel-based formulation is presented in order to consider the non …

A novel smoothed empirical likelihood (EL) approach that incorporates kernel estimation of the area under the receiver …

robustness of any given classification algorithm against the sampling randomness. The effectiveness of the proposed approach is …

A mixed effects least squares support vector machine (LS-SVM) classifier is introduced to extend the standard LS-SVM … classifier for handling longitudinal data. The mixed effects LS-SVM model contains a random intercept and allows to classify …

In the field of classification, the support vector machine (SVM) pursues a large margin between two classes. The margin … between the expectile value and the asymmetric squared loss, an asymmetric least squares SVM (aLS-SVM) is proposed. The … proposed aLS-SVM can also be regarded as an extension to the LS-SVM and the L2-SVM. Theoretical analysis and numerical …

q∈(0,1). The standard SVM [Vapnik, V., 1995. The Nature of Statistical Learning Theory. Springer, NY.] minimizes the … hinge loss function subject to the L2-norm penalty. Recently, L1-norm SVM (L1-SVM) [Bradley, P., Mangasarian, O., 1998 … functions are non-convex. This paper addresses the difficult optimization problems of fractional-norm SVM by introducing a new …