We consider kernel-based learning methods for regression and analyze what happens to the risk minimizer when new variables, statistically independent of input and target variables, are added to the set of input variables. This problem arises, for example, in the detection of causality relations between two time series. We find that the risk minimizer remains unchanged if we constrain the risk m...

A new numerical technique based on radial basis functions (RBFs) is presented for fitting a vector field tangent to the sphere, S2, from samples of the field at “scattered” locations on S 2. The method naturally provides a way to decompose the reconstructed field into its individual Helmholtz–Hodge components, i.e., into divergence-free and curl-free parts, which is useful in many applications ...

From the advent of the application of satellite imagery to land cover mapping, one of the growing areas of research interest has been in the area of image classification. Image classifiers are algorithms used to extract land cover information from satellite imagery. Most of the initial research has focussed on the development and application of algorithms to better existing and emerging classif...

The dynamic problems of a microactuator with a single edge crack are numerically formulated using radial basis functions. The microactuator model incorporates the taper ratio, electrode shapes, and crack length, all of which govern the dynamic behavior of microactuators. To optimize the design of a microactuator, many characteristics of various shaped cantilevers and curved electrodes are also ...

In standard equispaced finite difference (FD) formulas, symmetries can make the order of accuracy relatively high compared to the number of nodes in the FD stencil. With scattered nodes, such symmetries are no longer available. The generalization of compact FD formulas that we propose for scattered nodes and radial basis functions (RBFs) achieves the goal of still keeping the number of stencil ...

International Journal of Control Publication details, including instructions for authors and subscription information: http://www.informaworld.com/smpp/title~content=t713393989 Practical identification of NARMAX models using radial basis functions S. Chen a; S. A. Billings b; C. F. N. Cowan a; P. M. Grant a a Department of Electrical Engineering, University of Edinburgh, Edinburgh, Scotland, U....

This paper presents a new method for the fast evaluation of univariate radial basis functions of the form s(x) = ∑N n=1 dnφ(|x− xn|) to within accuracy . The method can be viewed as a generalization of the fast multipole method in which calculations with far field expansions are replaced by calculations involving moments of the data. The method has the advantage of being adaptive to changes in ...

In this paper we deal with the problem of reconstructing surfaces from unorganized sets of points, while capturing the significant geometry details of the modelled surface, such as edges, flat regions and corners. This is obtained by exploiting the good approximation capabilities of the Radial Basis Functions (RBF), the local nature of the method proposed in [1], and introducing information on ...

We apply computer vision methods to the task of automatically predicting human attractiveness from frontal face images. A dataset of thousands of images and corresponding scores was obtained from a popular website that asks viewers to rate the attractiveness of the people appearing in the images. Using a combination of radial basis functions and specialized feature detectors, we achieved modera...

We define a generalized distance function on an unoriented 3D point set and describe how it may be used to reconstruct a surface approximating these points. This distance function is shown to be a Mahalanobis distance in a higher-dimensional embedding space of the points, and the resulting reconstruction algorithm a natural extension of the classical Radial Basis Function (RBF) approach. Experi...