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...

This paper explores the possibilities of point cloud reduction using  insensitive support vector regression (-SVR).  -SVR is a technique that can carry out the regression using different kernel functions (sigmoid, radial basis function, B-spline, spline, etc.) and it is suitable for detection of flat regions and regions with high curvature in scanned data. Using  -SVR the density of preserv...

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...