When faced with unpredictable environments, the human motor system has been shown to develop optimized adaptation strategies that allow for online adaptation during the control process. Such online adaptation is to be contrasted to slower over-trial learning that corresponds to a trial-by-trial update of the movement plan. Here we investigate the interplay of both processes, i.e., online adapta...

In Bishop-style constructive algebra it is known that if a module over a commutative ring has a Noetherian basis function, then it is Noetherian. Using countable choice we prove the reverse implication for countable and strongly discrete modules. The Hilbert basis theorem for this specific class of Noetherian modules, and polynomials in a single variable, follows with Tennenbaum’s celebrated ve...

The design of radial basis function networks (RBF) is rather complex because of the great number of parameters that must be adjusted : positioning and number of kernels, choice of the distance type and centre widths, weight values. This article details these points in the framework of classification tasks with a partitioning approach : the global K-class problem is split into K 2-class sub-prob...

This paper describes a face recognition system that uses partial face images (for example, eye, nose, and ear images) for input data. The recognition technique is based on using Radial Basis Function (RBF) networks. As compared with using a standard backpropagation (BP) learning algorithm, the RBF networks are far superior for the face recognition task. From the experimental results of face rec...

This paper reports initial research on supporting Visually Mediated Interaction (VMI) by developing generic expression models and person-specific and generic gesture models for the control of active cameras. We investigate the recognition of both head pose and expression using simple generalisation of trained generic models using Radial Basis Function (RBF) networks. Then we go on to describe a...

Using radial basis function networks for function approximation tasks suffers from unavailable knowledge about an adequate network size. In this work, a measuring technique is proposed which can control the model complexity and is based on the correlation coefficient between two basis functions. Simulation results show good performance and, therefore, this technique can be integrated in the RBF...

Support Vector Machines (SVMs) have been applied to solve the classification of volatile organic compounds (VOC) data in some recent studies. SVMs provide good generalization performance in detection and classification of VOC data. However, in many applications involving VOC data, it is not unusual for additional data, which may include new classes, to become available over time, which then req...

In this paper we introduce adaptive vision techniques used, for example, in video-conferencing applications. Radial Basis Function (RBF) networks have been trained for gesture-based communication with colour/motion cues to direct face detection and capture ‘attentional frames’. These focus the processing for Visually Mediated Interaction via an appearance-based approach with Gabor filter coeffi...

In this work several attemps are proposed for extracting the Jominy hardness pro les of steel directly from the steel chemical composition. It is essentially a black{box modelling problem: the neural approach seems powerful, especially compared with classical models commonly found in literature. Several network structures have been implemented, which all can be viewed under the uni cation parad...

A method for optimal adaptive setting of pulse-positionmodulation pulse detection thresholds, which minimizes the total probability of error for the dynamically fading optical free space channel, is presented. The threshold’s adaptive setting, in response to varying channel conditions, results in orders of magnitude improvement in probability of error, as compared to use of a fixed threshold. T...