Metal magnetic memory (MMM) signals can reflect stress concentration and cracks on the surface of ferromagnetic components, but the traditional criteria used to distinguish the locations of these stress concentrations and cracks are not sufficiently accurate. In this study, 22 indices were extracted from the original MMM signals, and the diagnosis results of 4 kernel functions of support vector...

The report presents a series of numerical experiments concerning application of Support Vector Machines for the two class spatial data classification. The main attention is paid to the variability of the results by changing hyperparameters: bandwidth of the radial basis function kernel and C parameter. Training error, testing error and number of support vectors are plotted against hyperparamete...

Conventionally, a radial basis function (RBF) network is constructed by obtaining cluster centers of basis function by maximum likelihood learning. This paper proposes a novel learning algorithm for the construction of radial basis function using sensitivity analysis. In training, the number of hidden neurons and the centers of their radial basis functions are determined by the maximization of ...

In this paper, a machine learning-based system that could provide automated short-term solar flares prediction is presented. This system accepts two sets of inputs: McIntosh classification of sunspot groups and solar cycle data. In order to establish a correlation between solar flares and sunspot groups, the system explores the publicly available solar catalogues from the National Geophysical D...

Remote sensing data is a collection of images and interpretation of information about an object, area, or event without any physical contact with it. Aircraft and satellites are common remote sensing platforms for earth and its natural sources. Remote sensing’s ability to identify and monitor land surfaces and environmental conditions expanded over years with remote sensed data being essential ...

AbstractThis paper describes an experiment on face recognition using a simple feature vector and Support Vector Machine (SVM) classifier. Polynomial and Radial Basis Function (RBF) kernels of SVM are used for classification. The dataset in this experiment consists of a set of images of eight different faces (eight classes) containing ten different images for a single class. The experiment is pe...

Meteorological volumetric data are used to detect thunderstorms that are the cause of most of the summer severe weathers. There are systems that may convert the volumetric data into a set of derived products. Based on these derived features, this work compares three classifiers to determine which approach will best classify a storm cell data set coming from Environment Canada. The criterion for...

A better submitted version of this paper is available at http://eric.univ-lyon2.fr/~ oteytaud. This report only completes the paper for some results of lower interest and we suggest that interested readers rst read the submitted version and only completes this reading by this paper if they want more information on some results not detailed in the submitted version. This report was rst written i...

The efficiency of the orthogonal least squares (OLS) method for training approximation networks is examined using the criterion of energy compaction. We show that the selection of basis vectors produced by the procedure is not the most compact when the approximation is performed using a nonorthogonal basis. Hence, the algorithm does not produce the smallest possible networks for a given approxi...