This paper presents a simple estimate to determine the width of Gaussian kernel with the adaptive scaling technique. The Gaussian kernel is widely employed in Radial Basis Function (RBF) network, Support Vector Machine (SVM), Least Squares Support Vector Machine (LS-SVM), Kriging, and so on. It is widely known that the width of the Gaussian kernel in these machine learning techniques plays an i...

Lung cancer is one of the diseases responsible for a large number of cancer related death cases worldwide. The recommended standard for screening and early detection of lung cancer is the low dose computed tomography. However, many patients diagnosed die within one year, which makes it essential to find alternative approaches for screening and early detection of lung cancer. We present computat...

Most current model reference adaptive control methods rely on parametric adaptive elements, in which the number of parameters of the adaptive element are fixed a-priori, often through expert judgment. Examples of such adaptive elements are the commonly used Radial Basis Function Neural Networks (RBF-NN) with centers allocated a priori based on the expected operating domain. If the system operat...

Gaussian Process (GP) has gained much attention in cosmology due to its ability reconstruct cosmological data a model-independent manner. In this study, we compare two methods for GP kernel selection: Approximate Bayesian Computation (ABC) Rejection and nested sampling. We analyze three types of data: cosmic Chronometer (CC), Type Ia Supernovae (SNIa), Gamma Ray Burst (GRB), using five function...

Abstract This article introduces a method for realizing the Gaussian activation function of radial-basis (RBF) neural networks with their hardware implementation on field-programmable gaits area (FPGAs). The results modeling FPGA chips different families have been presented. RBF various topologies synthesized and investigated. component implemented by this algorithm is an network four neurons l...

Greenhouses are essential for agricultural production in unfavorable climates. Accurate temperature predictions critical controlling Heating, Ventilation, Air-Conditioning, and Dehumidification (HVACD) lighting systems to optimize plant growth reduce financial losses. In this study, several machine models were employed predict indoor air an even-span Mediterranean greenhouse. Radial Basis Funct...

In this paper, we propose a meshfree method based on the Gaussian radial basis function (RBF) to solve both classical and fractional PDEs. The proposed takes advantage of analytical Laplacian functions so as accommodate discretization in single framework avoid large computational cost for numerical evaluation derivatives. These important merits distinguish it from other methods Moreover, our is...

We develop forecasting models based on the neural approach for the forecasting of the bond price time series provided by the VUB bank and make their comparisons of the forecast accuracy with the class of the statistical ARCH-GARCH models. There is a limited statistical or computer science theory on how to design the architecture of the RBF networks for some specific nonlinear financial or econo...

Traditional classiication approaches generalize poorly on image classiication tasks, because of the high dimensionality of the feature space. This paper shows that Support Vector Machines (SVM) can generalize well on diicult image classiication problems where the only features are high dimensional histograms. Heavy-tailed RBF kernels of the form K(x; y) = e ? P i jx a i ?y a i j b with a 1 and ...

The study develops the displacement error recovery method in a mesh free environment for finite element solution employing radial point interpolation (RPI) technique. RPI technique uses basis functions (RBF), along with polynomials to interpolate fields node patch and recovers field. global local errors are quantified both energy L2 norms from post-processed considers multi-quadrics/gaussian/th...