BACKGROUND Support vector machine (SVM) has been widely used as accurate and reliable method to decipher brain patterns from functional MRI (fMRI) data. Previous studies have not found a clear benefit for non-linear (polynomial kernel) SVM versus linear one. Here, a more effective non-linear SVM using radial basis function (RBF) kernel is compared with linear SVM. Different from traditional stu...

A linear autoregressive/moving-average model was developed to describe the dynamic relationship between mean arterial pressure (MAP), renal sympathetic nerve activity (SNA) and renal blood flow (RBF) in conscious rabbits. The RBF and SNA to the same kidney were measured under resting conditions in a group of eight rabbits. Spectral analysis of the data sampled at 0.4 Hz showed that the low-pass...

PURPOSE To investigate how neuronal nitric oxide synthase (nNOS) contributes to regulation of the retinal circulation during rest and flicker stimulation in cats. METHODS Using laser Doppler velocimetry, we measured the vessel diameter and blood velocity simultaneously and calculated the retinal blood flow (RBF) in feline first-order retinal arterioles. After intravitreal injections of Nω-Nit...

Learning in radial basis function (RBF) networks is the topic of this paper. Particularly we address the problem of intialisation the centers and scaling parameters in RBF networks utilizing classiication tree algorithms. This method was introduced by Kubat in 1998. Algorithms for the calculation of the centers and scaling parameters in an RBF network are presented and numerical results for the...

The Radial Basis Function method (RBF) can be used not only for reconstruction of a surface from scattered data but for reconstruction of damaged images, filling gaps and for restoring missing data in images, too. The basic idea of reconstruction algorithm with RBF and very interesting results from reconstruction of images damaged by noise are presented. Feasibility of the RBF method for image ...

| Although our previous model CLARION has shown some measure of success in reactive sequential decision making tasks by utilizing a hybrid architecture which uses both procedural and declarative learning, it suuers from a number of problems because of its use of back propagation networks. CLARION-RBF is a more parsimonious architecture that remedies some of the problems exhibited in CLARION by ...

The paper presents a two-level learning method for radial basis function (RBF) networks. A regularized orthogonal least squares (ROLS) algorithm is employed at the lower level to construct RBF networks while the two key learning parameters, the regularization parameter and the RBF width, are optimized using a genetic algorithm (GA) at the upper level. Nonlinear time series modeling and predicti...

Chronic ANG II infusion in rodents is widely used as an experimental model of hypertension, yet very limited data are available describing the resulting blood pressure-renal blood flow (BP-RBF) relationships in conscious rats. Accordingly, male Sprague-Dawley rats (n = 19) were instrumented for chronic measurements of BP (radiotelemetry) and RBF (Transonic Systems, Ithaca, NY). One week later, ...

In this paper, different methods for training radial basis function (RBF) networks for regression problems are described and illustrated. Then, using data from the DELVE archive, they are empirically compared with each other and with some other well known methods for machine learning. Each of the RBF methods performs well on at least one DELVE task, but none are as consistent as the best of the...