These kernels combine the benefits of two other important classes of kernels: the homogeneous additive kernels (e.g. the χ2 kernel) and the RBF kernels (e.g. the exponential kernel). However, large scale problems require machine learning techniques of at most linear complexity and these are usually limited to linear kernels. Recently, Maji and Berg [2] and Vedaldi and Zisserman [4] proposed exp...

The efficiency of pattern recognition (PR) systems using RBF neural networks to implement their recognition function, depends a lot by the training algorithms of these neural networks and especially, by the specific techniques (e.g., supervised, clustering techniques etc.) used for RBF center positioning. Having as starting point the basic property of genetic algorithms (GA) to represent global...

| Adaptive methods for demodulating multiuser communication in a Direct-Sequence Spread-Spectrum Multiple-Access (DS/SSMA) environment are investigated. In this setting the noise is characterized as being the sum of the interfering users' signals and additive Gaussian noise. The optimal receiver for DS/SSMA systems has a complexity that is exponential in the number of users. Adaptive Radial Bas...

Speaker identification is the process of determining which registered speaker provides a given utterance. Speaker identification required to make a claim on the identity of speaker from the Ns trained speaker in its user database. In this study, we propose the combination of clustering algorithm and the classification technique – subtractive and Radial Basis Function (RBF). The proposed techniq...

Standard particle swarm optimization (SPSO) algorithm was modified by escape strategy of the particle velocity, and an escape PSO (EPSO) was proposed to overcome the shortcomings of being trapped in local optima because of premature convergence. To enhance the performance of radial basis function (RBF) neural network, the EPSO is combined with RBF neural network to form a EPSON hybrid algorithm...

Radial basis function (RBF) approximation, is a new extremely powerful tool that is promising for high-dimensional problems, such as those arising from pricing of basket options using the Black-Scholes partial differential equation. The main problem for RBF methods have been ill-conditioning as the RBF shape parameter becomes small, corresponding to flat RBFs. This thesis employs a recently dev...

The retinoblastoma gene Rb is the prototype tumor suppressor and is conserved in Drosophila. We use the developing fly retina as a model system to investigate the role of Drosophila Rb (rbf) during differentiation. This report shows that mutation of rbf and rhinoceros (rno), which encodes a PHD domain protein, leads to a synergistic delay in photoreceptor cell differentiation in the developing ...

An important problem in engineering is the identification of nonlinear systems, among them radial basis function neural networks (RBF-NN) using Gaussian activation functions models, which have received particular attention due to their potential to approximate nonlinear behavior. Several design methods have been proposed for choosing the centers and spread of Gaussian functions and training the...

This paper presents modification of Radial Basis Function Artificial Neural Network (RBF ANN)-based temperature compensation models for Interferometric Fiber Optical Gyroscopes (IFOGs). Based on the mathematical expression of IFOG output, three temperature relevant terms are extracted, which include: (1) temperature of fiber loops; (2) temperature variation of fiber loops; (3) temperature produ...

We present a new constructive algorithm for building Radial-Basis-Function (RBF) network classiiers and a tree based associated algorithm for fast processing of the network. This method, named Constructive Tree Radial-Basis-Function (CTRBF), allows to build and train a RBF network in one pass over the training data set. The training can be in supervised or unsupervised mode. Furthermore, the al...