0957-4174/$ see front matter 2011 Elsevier Ltd. A doi:10.1016/j.eswa.2011.09.082 ⇑ Corresponding author. Tel.: +9

This paper presents an alternating minimization algorithm used to train radial basis function networks. The algorithm is a modiication of an interior point method used in solving primal linear programs. The resulting algorithm is shown to have a convergence rate on the order of p nL iterations where n is a measure of the network size and L is a measure of the resulting solution's accuracy.

Support Vector Machines are used for time series prediction and compared to radial basis function networks. We make use of two diierent cost functions for Support Vectors: training with (i) an insensitive loss and (ii) Huber's robust loss function and discuss how to choose the regularization parameters in these models. Two applications are considered: data from (a) a noisy (normal and uniform n...

Selecting a set of features which is optimal for a given classiication task is one of the central problems in machine learning. We address the problem using the exible and robust lter technique EUBAFES. EUBAFES is based on a feature weighting approach which computes binary feature weights and therefore a solution in the feature selection sense and also gives detailed information about feature r...

For estimation of monthly precipitation, considering the intricacy and lack of accurate knowledge about the physical relationships, black box models usually are used because they produce more accurate values. In this article, a hybrid black box model, namely ANN-RBF, is proposed to estimate spatiotemporal value of monthly precipitation. In the first step a Multi Layer Perceptron (MLP) network i...

A novel approach for the classification of both balanced and imbalanced dataset is developed in this paper by integrating the best attributes of radial basis function networks and differential evolution. In addition, a special attention is given to handle the problem of inconsistency and removal of irrelevant features. Removing data inconsistency and inputting optimal and relevant set of featur...

In this chapter we present a 3-D visual object recognition system for an autonomous mobile robot. This object recognition system performs the following three tasks: Object localisation in the camera images, feature extraction, and classification of the extracted feature vectors with hierarchical radial basis function (RBF) networks.

Joachim Feist , Ste en Gutjahr Neurotec Hochtechnologie GmbH Germany Email: [email protected] University of Karlsruhe Institute of Logic, Complexity and Deduction Systems Germany Email: [email protected] Abstract. In this paper we compare variants of elliptical basis function networks for classication tasks. The networks are introduced as density estimators and then modi ed towards RBF networks. ...