In this paper, we introduce a novel method of automatically finding the training set of RBF networks for morphing a prototype face to represent a new face. This is done by automatically specifying and adjusting corresponding feature points on a target face. The RBF networks are then used to transfer the muscles on the prototype face to the morphed face. The automatic adjusting of the feature po...

‎Radial basis functions (RBFs) are a powerful tool for approximating the solution of high-dimensional problems‎. ‎They are often referred to as a meshfree method and can be spectrally accurate‎. ‎In this paper, we analyze a new stable method for evaluating Gaussian radial basis function interpolants based on the eigenfunction expansion‎. ‎We develop our approach in two-dimensional spaces for so...

In this addition to the regular paper, we derive the required derivatives required to implement the informative prior from a simulator in PILCO [1]. First, for completeness, we repeat the derivation of the mean, covariance, and input-output covariance of the predictive mean of a Gaussian process (GP) when the prior mean is a radial basis function network (RBF). Then, we detail the partial deriv...

In this paper we propose a novel approach for modeling kernels in Radial Basis Function networks. The method provides an extra degree of flexibility to the kernel structure. This flexibility comes through the use of modifier functions applied to the distance computation procedure, essential for all kernel evaluations. Initially the classifier uses an unsupervised method to construct the network...

In this paper, a hybrid network based on the combination of Radial Basis Function Networks (RBFNs) and Gaussian Mixture Models (GMMs) is proposed and used for speaker recognition. The hybrid network is a hierarchical one, where a GMM is built for each speaker and an RBFN is built for each group of speakers. The GMMs and RBFNs are trained independently. The RBFNs are used as a rst stage coarse c...

This paper presents a new data-driven approach for modeling haptic responses of textured surfaces with homogeneous anisotropic grain. The approach assumes unconstrained tool-surface interaction with a rigid tool for collecting data during modeling. The directionality of the texture is incorporated in modeling by including 2 dimentional velocity vector of user’s movement as an input for the data...

An orthogonal forward selection (OFS) algorithm based on the leaveone-out (LOO) criterion is proposed for the construction of radial basis function (RBF) networks with tunable nodes. This OFS-LOO algorithm is computationally efficient and is capable of identifying parsimonious RBF networks that generalise well. Moreover, the proposed algorithm is fully automatic and the user does not need to sp...

A novel two stage Improved Radial Basis Function (IRBF) neural network for the damage identification of a multimember structure in the frequency domain is presented. The improvement of the proposed IRBF network is carried out in two stages. Conventional RBF network is used in the first stage for preliminary damage prediction and in the second stage reduced search space moving technique is used ...

This paper proposes an artificial neural network RBF to classification using feature descriptors. The theoretical and practical aspects of theory F distributions with different degrees of freedom introduced. The distribution F densities are similar in shape, making it difficult to identify the differences between the two densities. This paper is concerned with separating these same probability ...

Monitoring lightning electromagnetic pulses (sferics) and other terrestrial as well as extraterrestrial transient radiation signals is of considerable interest for practical and theoretical purposes in astroand geophysics as well as meteorology. Managing a continuous flow of data, automisation of the detection and classification process is important. Features based on a combination of wavelet a...