Noncommutative reproducing kernel Hilbert spaces

Real reproducing kernel Hilbert spaces

P (α) = C(α, F (x, y)) = αF (x, x) + 2αF (x, y) + F (x, y)F (y, y), which is ≥ 0. In the case F (x, x) = 0, the fact that P ≥ 0 implies that F (x, y) = 0. In the case F (x, y) 6= 0, P (α) is a quadratic polynomial and because P ≥ 0 it follows that the discriminant of P is ≤ 0: 4F (x, y) − 4 · F (x, x) · F (x, y)F (y, y) ≤ 0. That is, F (x, y) ≤ F (x, y)F (x, x)F (y, y), and this implies that F ...

Some Properties of Reproducing Kernel Banach and Hilbert Spaces

This paper is devoted to the study of reproducing kernel Hilbert spaces. We focus on multipliers of reproducing kernel Banach and Hilbert spaces. In particular, we try to extend this concept and prove some related theorems. Moreover, we focus on reproducing kernels in vector-valued reproducing kernel Hilbert spaces. In particular, we extend reproducing kernels to relative reproducing kernels an...

Online learning in Reproducing Kernel Hilbert Spaces

Reproducing kernel Hilbert spaces of Gaussian priors

We review definitions and properties of reproducing kernel Hilbert spaces attached to Gaussian variables and processes, with a view to applications in nonparametric Bayesian statistics using Gaussian priors. The rate of contraction of posterior distributions based on Gaussian priors can be described through a concentration function that is expressed in the reproducing Hilbert space. Absolute co...

Reproducing Kernel Hilbert Spaces - Two Brief Reviews

This TR contains two brief reviews which will appear in the Proceedings of the 13th IFAC Symposium on System Identification (SYSID 2003), Rotterdam, August 2003. They are the basis for two talks in the invited session WeP02-Reproducing Kernels 1, and were prepared within the space limitations of the Proceedings. They are primarily based on work of the author and collaborators. There are many re...