Regularization with radial basis functions is an effective method in many machine learning applications. In recent years classes of radial basis functions with compact support have been proposed in ...

SCOTT 1 has derived a radial distribution function from a packing of about 1,000 spheres by measuring the Cartesian co-ordinates of each sphere and calculating the individual distribution functions of ...

The series expansion of the prolate radial functions of the second kind, expressed in terms of the spherical Neumann functions, converges very slowly when the spheroid's surface coordinate ξ ...