The Hermite Radial Basis Functions (HRBF) Implicits reconstruct an implicit function which interpolates or approximates scattered multivariate Hermite data (i.e., unstructured points and their corresponding normals). Experiments suggest that HRBF Implicits allow the reconstruction of surfaces rich in details and behave better than previous related methods under coarse and/or nonuniform sampling...

In this paper, some meshless methods based on the local Newton basis functions are used to solve some time dependent partial differential equations. For stability reasons, used variably scaled radial kernels for constructing Newton basis functions. In continuation, with considering presented basis functions as trial functions, approximated solution functions in the event of spatial variable wit...

A Bayesian framework for the analysis of radial basis functions (RBF) is proposed which readily accommodates uncertainty in the dimension of the model. A distribution is deened over the space of all RBF models of a given basis function and posteriors are computed using reversible jump Markov chain Monte Carlo samplers (Green, 1995). This alleviates the need to select one particular architecture...

Support Vector Machines are used for time series prediction and compared to radial basis function networks. We make use of two diierent cost functions for Support Vectors: training with (i) an insensitive loss and (ii) Huber's robust loss function and discuss how to choose the regularization parameters in these models. Two applications are considered: data from (a) a noisy (normal and uniform n...

This paper presents a preliminary exploration showing the surprising effect of extreme parameter values used by Support Vector Machine (SVM) classifiers for identifying objects in images. The Radial Basis Function (RBF) kernel used with SVM classifiers is considered to be a state-of-the-art approach in visual object classification. Standard tuning approaches apply a relative narrow window of va...

Recently, the open-set recognition problem has received more attention by the machine learning community given that most classification problems in practice require an open-set treatment. Thus far, many classifiers were mostly developed for the closed-set scenario, i.e., the scenario of classification in which it is assumed that all test samples belong to one of the classes the classifier was t...

In this paper, we propose the spectral collocation method based on radial basis functions to solve the fractional Bagley-Torvik equation under uncertainty, in the fuzzy Caputo's H-differentiability sense with order ($1< nu < 2$). We define the fuzzy Caputo's H-differentiability sense with order $nu$ ($1< nu < 2$), and employ the collocation RBF method for upper and lower approximate solutions. ...

In this communication, we analyze several regularized types of Radial Basis Function (RBF) Networks for crop classification using hyperspectral images. We compare the regularized RBF neural network with Support Vector Machines (SVM) using the RBF kernel, and AdaBoost Regularized (ABR) algorithm using RBF bases, in terms of accuracy and robustness. Several scenarios of increasing input space dim...