Radial basis function networks (RBF) are efficient general function approximators. They show good generalization performance and they are easy to train. Due to theoretical considerations RBFs commonly use Gaussian activation functions. It has been shown that these tight restrictions on the choice of possible activation functions can be relaxed in practical applications. As an alternative differ...

1.1 Background In ordinary channel equalizer and multi-antenna system, many types of detecting methods have been proposed to compensate the distorted signals or recover the original symbols of the desired user [1]-[3]. For channel equalization, transversal equalizers (TEs) and decision feedback equalizers (DFEs) are commonly used as a detector to compensate the distorted signals [2]. It is well...

Crop seed yield modeling and prediction can act as a key approach in the precision agriculture industry, enabling reliable assessment of effectiveness agro-traits. Here, multiple machine learning (ML) techniques are employed to predict sesame (Sesamum indicum L.) yields (SSY) using agro-morphological features. Various ML models were applied, coupled with PCA (principal component analysis) metho...

The ability of combining near-infrared (NIR) Raman spectroscopy with support vector machines (SVM) for improving multi-class classification between different histopathological groups in tissues was evaluated in this study. A total of 105 colonic tissue specimens from 59 patients including 41 normal, 18 hyperplastic polyps and 46 adenocarcinomas were used for this purpose. A rapid-acquisition di...

This work investigates the optimization and validation of Support Vector Machines (SVMs) for land cover classification from multispectral aerial imagery and lidar data. For the optimization step, a new method based on a curve fitting technique was applied to minimize the grid search for the Gaussian Radius Basis Function (RBF) parameters. The validation step was based on two experiments. In the...

The appearance of a human face rigorously changes with respect to age that makes Age Classification as a more challenging task. The algorithms such as, K-Nearest Neighbor (K-NN), Support Vector Machine (SVM), Radial Basis Function (RBF), motivated many Face Researchers to focus their attention in classifying the human age into various age groups. The Classification rate produced by these existi...

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 ...

Based on biological data we examine the ability of Sup port Vector Machines SVMs with gaussian polyno mial and tanh kernels to learn and predict the nonlin ear dynamics of single biological neurons We show that SVMs for regression learn the dynamics of the pyloric dilator neuron of the australian cray sh and we deter mine the optimal SVM parameters with regard to the test error Compared to conv...

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 ...

As a kernel independent algorithm, multilevel Green’s function interpolation method (MLGFIM) [1] [2] has been developed for electromagnetic problems from low frequency to fullwave simulation. This method inherits the multilevel tree structure of multilevel fast multipole algorithm (MLFMA) and adopts the interpolation technique of pre-corrected fast Fourier transform (PFFT). Compare with other f...