ORBIT is a MATLAB-based toolkit for black-box and grey-box modeling of non-linear dynamic systems. The model representation is based on multiple local models valid in di erent operating regimes, that are smoothly blended into a global non-linear model. ORBIT is a computeraided modeling environment that supports the interactive development of regime-based models on the basis of a mixture of empi...

This paper presents a novel variational approach for the segmentation of diffusion tensor images (DTI). After a certain fiber bundle has been tracked by means of an arbitrary fiber tracking algorithm, we suggest to use the DTI segmentation algorithm to better determine the true borders of the fiber bundle. Specifically, we perform kernel density estimations of the probability density functions ...

An integrated approach for multi-spectral segmentation of MR images is presented. This method is based on the fuzzy c-means (FCM) and includes bias field correction and contextual constraints over spatial intensity distribution and accounts for the non-spherical cluster's shape in the feature space. The bias field is modeled as a linear combination of smooth polynomial basis functions for fast ...

The problem of designing the regularization term and regularization parameter for linear regression models is discussed. Previously, we derived an approximation to the generalization error called the subspace information criterion (SIC), which is an unbiased estimator of the generalization error with finite samples under certain conditions. In this paper, we apply SIC to regularization learning...

This paper is concerned with the solution of large-scale linear discrete ill-posed problems. The determination of a meaningful approximate solution of these problems requires regularization. We discuss regularization by the Tikhonov method and by truncated iteration. The choice of regularization matrix in Tikhonov regularization may significantly affect the quality of the computed approximate s...

Abstract In general, data contain noises which come from faulty instruments, flawed measurements or communication. Learning with in the context of classification regression is inevitably affected by data. order to remove greatly reduce impact noises, we introduce ideas fuzzy membership functions and Laplacian twin support vector machine (Lap-TSVM). A formulation linear intuitionistic (IFLap-TSV...