In this paper we evaluate the classiication accuracy of four statistical and three neural network classiiers for two image based pattern classiication problems. These are ngerprint classiication and optical character recognition (OCR) for isolated handprinted digits. The evaluation results reported here should be useful for designers of practical systems for these two important commercial appli...

An application of machine learning algorithms to the clustering and classification of chemical data concerning heroin seizures is presented. The data concerns the chemical constituents of heroin as given by a gas chromatography analysis. Following a preprocessing step, where the six initial constituents are reduced to only two significant features, the data are clustered in order to find natura...

This paper reports preliminary progress on a principled approach to modelling nonstationary phenomena using neural networks. We are concerned with both parameter and model order complexity estimation. The basic methodology assumes a Bayesian foundation. However t o allow the construction of pragmatic models, successive approximations have to be made l o permit computational tractibility. The lo...

In a deregulated power market, bidding decisions rely on good market clearing price prediction. One of the common forecasting methods is Gaussian radial basis function (GRBF) networks that approximate input–output relationships by building localized Gaussian functions (clusters). Currently, a cluster uses all the input factors. Certain input factors, however, may not be significant and should b...

In this paper a dynamically adaptive neural network architecture is investigated for robot behavior learning. Specifically, a so-called “Grow When Required” network (GWR) is used to dynamically cluster the sensor-motor training data for determining the centers of a radial basis function network (RBF), and then the RBF network is trained for acquiring and performing the required behaviors. We il...

In this work we want to analyse the behaviour of two classic Artificial Neural Network models respect to a data complexity measures. In particular, we consider a Radial Basis Function Network and a MultiLayer Perceptron. We examine the metrics of data complexity known as Measures of Separability of Classes over a wide range of data sets built from real data, and try to extract behaviour pattern...

| This paper is composed of two parts. The rst part surveys the literature regarding optimum nonlinear l-tering from the (continuous-time) stochastic analysis point of view, and the other part explores the impact of recent applications of neural networks (in a discrete-time context) to nonlinear ltering. In particular, the results obtained by using a regularized form of radial basis function (R...

Even though radial basis function networks are known to have good prediction accuracy in several domains, it is not known to decide a proper sample size like other data mining algorithms, so the task of deciding proper sample sizes for the networks tends to be arbitrary. As the size of samples grows, the improvement in error rates becomes better slowly. But we cannot use larger and larger sampl...

A functional equivalence of feed-forward networks has been proposed to reduce the search space of learning algorithms. A novel genetic learning algorithm for RBF networks and perceptrons with one hidden layer that makes use of this theoretical property is proposed. Experimental results show that our procedure outperforms the standard genetic learning. Key-Words: Feedforward neural networks, gen...

Bio-absorbent palm fiber was applied for removal of cationic violet methyl dye from water solution. For this purpose, a solid phase extraction method combined with the artificial neural network (ANN) was used for preconcentration and determination of removal level of violet methyl dye. This method is influenced by factors such as pH, the contact time, the rotation speed, and the adsorbent dosag...