This paper presents a receiver lter, based on V olterra functions, for a direct-sequence code-division-multiple-access (DS-CDMA) system. The envisaged application would be cellular tele-phony, where the receiver will be required to be adaptive due to changes to the channel characteristics. The results obtained from the V olterra lter are compared against the linear minimum-mean-square-error (MM...

For binary classification we establish learning rates up to the order of n−1 for support vector machines (SVMs) with hinge loss and Gaussian RBF kernels. These rates are in terms of two assumptions on the considered distributions: Tsybakov’s noise assumption to establish a small estimation error, and a new geometric noise condition which is used to bound the approximation error. Unlike previous...

In this work we present an active learning methodology for training the probabilistic RBF (PRBF) network. It is a special case of the RBF network, and constitutes a generalization of the Gaussian mixture model. We propose an incremental method for semi-supervised learning based on the Expectation-Maximization (EM) algorithm. Then we present an active learning method that iteratively applies the...

Extracting rules from RBFs is not a trivial task because of nonlinear functions or high input dimensionality. In such cases, some of the hidden units of the RBF network have a tendency to be “shared” across several output classes or even may not contribute to any output class. To address this we have developed an algorithm called LREX (for Local Rule EXtraction) which tackles these issues by ex...

Differentiation matrices obtained with infinitely smooth radial basis function (RBF) collocation methods have, under many conditions, eigenvalues with positive real part, preventing the use of such methods for time-dependent problems. We explore this difficulty at theoretical and practical levels. Theoretically, we prove that differentiation matrices for conditionally positive definite RBFs are...

The question in the title is answered using tools of potential theory. Convergence and divergence rates of interpolants of analytic functions on the unit interval are analyzed. The starting point is a complex variable contour integral formula for the remainder in RBF interpolation. We study a generalized Runge phenomenon and explore how the location of centers affects convergence. Special atten...

The image-based plant identification challenge was focused on tree, herbs and ferns species identification based on different types of images. The aim of the task was to produce relevant species for each observation of a plant of the test dataset. We have elaborated a viewpoints combined classification method for this challenge. We have applied dense SIFT for feature detection and description; ...

Dynamic Voltage Scaling requires a model of the critical path to allow the proper setting of the supply voltage to meet a target frequency. In this paper, we propose a new technique to model the supply and delay relationship of a critical path which is based on Gaussian Radial Basis Functions (RBF) neural network. This modeling technique is able to successfully capture the process and temperatu...

The probabilistic RBF network (PRBF) is a special case of the RBF network and constitutes a generalization of the Gaussian mixture model. In this paper we propose a semi-supervised learning method for PRBF, using labeled and unlabeled observations concurrently, that is based on the expectation–maximization (EM) algorithm. Next we utilize this method in order to implement an incremental active l...

A new approach is proposed to recover the relative magnitude of Gaussian curvature from three shading images using neural network. Under the assumption that the test object has the same reflectance property as the calibration sphere of known shape, RBF neural network learns the mapping of three observed image intensities to the corresponding coordinates of (x, y). Three image intensities at the...