In the context of regression analysis with penalised linear models (such as RBF networks) certain model selection criteria can be diierentiated to yield a re-estimation formula for the regularisation parameter such that an initial guess can be iteratively improved until a local minimum of the criterion is reached. In this paper we discuss some enhancements of this general approach including imp...

Data acquired from ultrasound examinations is of interest not only for the physician, but also for the patient. While the physician uses the ultrasound data for diagnostic purposes the patient might be more interested in beautiful images in the case of prenatal imaging. Ultrasound data is noisy by nature and visually compelling 3D renderings are not always trivial to produce. This paper present...

The average diffusion propagator (ADP) obtained from diffusion MRI (dMRI) data encapsulates important structural properties of the underlying tissue. Measures derived from the ADP can be potentially used as markers of tissue integrity in characterizing several mental disorders. Thus, accurate estimation of the ADP is imperative for its use in neuroimaging studies. In this work, we propose a sim...

We examine the ability of radial basis functions (RBFs) to generalize. We compare the performance of several types of RBFs. We use the inverse dynamics of an idealized two-joint arm as a test case. We find that without a proper choice of a norm for the inputs, RBFs have poor generalization properties. A simple global scaling of the input variables greatly improves performance. We suggest some e...

Radial basis functions are tools for reconstruction of mul-tivariate functions from scattered data. This includes, for instance, reconstruction of surfaces from large sets of measurements, and solving partial diierential equations by collocation. The resulting very large linear N N systems require eecient techniques for their solution, preferably of O(N) or O(N log N) computational complexity. ...

In this paper, different models of the pressure buildup inside a hydraulic servoaxis are compared. These models are obtained using RBF networks, local linear models and support vector machines (SVMs), with a particular focus on the latter. For SVMs, a reduction method is derived, which allows to reduce the number of support vectors without losing the generalization abilities of the SVM. Experim...

We determine the asymptotic limit of the function computed by support vector machines (SVM) and related algorithms that minimize a regularized empirical convex loss function in the reproducing kernel Hilbert space of the Gaussian RBF kernel, in the situation where the number of examples tends to infinity, the bandwidth of the Gaussian kernel tends to 0, and the regularization parameter is held ...

Image classification is vital field of research in computer vision. Increasing rate of multimedia data, remote sensing and web photo gallery need a category of different image for the proper retrieval of user. Various researchers apply different approach for image classification such as segmentation, clustering and some machine learning approach for the classification of image. Content of image...

We introduce a method that aims to nd the global minimum of a continuous nonconvex function on a compact subset of IR d. It is assumed that function evaluations are expensive and that no additional information is available. Radial basis function interpolation is used to deene a utility function. The maximizer of this function is the next point where the objective function is evaluated. We show ...