Branching problems in reproducing kernel spaces
نویسندگان
چکیده
منابع مشابه
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 ...
متن کاملSeparability of reproducing kernel spaces
We demonstrate that a reproducing kernel Hilbert or Banach space of functions on a separable absolute Borel space or an analytic subset of a Polish space is separable if it possesses a Borel measurable feature map.
متن کاملReproducing kernel almost Pontryagin spaces
Article history: Received 16 May 2014 Accepted 1 August 2014 Submitted by R. Brualdi MSC: primary 46C20, 46E40 secondary 46E22, 54D35
متن کاملDistribution Embeddings in Reproducing Kernel Hilbert Spaces
The “kernel trick” is well established as a means of constructing nonlinear algorithms from linear ones, by transferring the linear algorithms to a high dimensional feature space: specifically, a reproducing kernel Hilbert space (RKHS). Recently, it has become clear that a potentially more far reaching use of kernels is as a linear way of dealing with higher order statistics, by embedding proba...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: Duke Mathematical Journal
سال: 2020
ISSN: 0012-7094
DOI: 10.1215/00127094-2020-0032