The problem of characterizing learnability is the most basic question of statistical learning theory. A fundamental and long-standing answer, at least for the case of supervised c...
Learnability in Valiant’s PAC learning model has been shown to be strongly related to the existence of uniform laws of large numbers. These laws define a distribution-free conver...
Gaussian kernels with flexible variances provide a rich family of Mercer kernels for learning algorithms. We show that the union of the unit balls of reproducing kernel Hilbert s...
The mortar finite element is an example of a non-conforming method which can be used to decompose and re-compose a domain into subdomains without requiring compatibility between th...
— We present an algorithm that determines the point on a convex parametric surface patch that is closest to a given (possibly moving) point. Any initial point belonging to the su...