We determine the asymptotic limit of the function computed by support vector machines (SVM) and related algorithms that minimize a regularized empirical convex loss function in the reproducing kernel Hilbert space of the Gaussian RBF kernel, in the situation where the number of examples tends to infinity, the bandwidth of the Gaussian kernel tends to 0, and the regularization parameter is held ...

In the classical Gaussian SVM classification we use the feature space projection transforming points to normal distributions with fixed covariance matrices (identity in the standard RBF and the covariance of the whole dataset in Mahalanobis RBF). In this paper we add additional information to Gaussian SVM by considering local geometry-dependent feature space projection. We emphasize that our ap...

This paper describes an approach for credit risk evaluation based on linear Support Vector Machines classifiers, combined with external evaluation and sliding window testing, with focus on application on larger datasets. It presents a technique for optimal linear SVM classifier selection based on particle swarm optimization technique, providing significant amount of focus on imbalanced learning...

We present an approximation scheme for support vector machine models that use an RBF kernel. A second-order Maclaurin series approximation is used for exponentials of inner products between support vectors and test instances. The approximation is applicable to all kernel methods featuring sums of kernel evaluations and makes no assumptions regarding data normalization. The prediction speed of a...

in this paper we use radial basis functions to solve multivariable integral equations. we use collocation method for implementation. numerical experiments show the accuracy of the method.

‎Radial basis functions (RBFs) are a powerful tool for approximating the solution of high-dimensional problems‎. ‎They are often referred to as a meshfree method and can be spectrally accurate‎. ‎In this paper, we analyze a new stable method for evaluating Gaussian radial basis function interpolants based on the eigenfunction expansion‎. ‎We develop our approach in two-dimensional spaces for so...

Authors of short papers such as letters or editorials often express complementary opinions, and sometimes contradictory ones, on related work in previously published articles. The MEDLINE® citations for such short papers are required to list bibliographic data on these “commented on” articles in a “CON” field. The challenge is to automatically identify the CON articles referred to by the author...