Results 91 to 100 of about 4,142 (221)
Abstract and Applied Analysis, 2014 A numerical method based on the reproducing kernel theorem is presented for the numerical solution of a three-point boundary value problem with an integral condition.
Jing Niu, Ping Li
doaj +1 more source
Repelled Point Processes With Application to Numerical Integration
Scandinavian Journal of Statistics, EarlyView.ABSTRACT We look at Monte Carlo numerical integration from a stochastic geometry point of view. While crude Monte Carlo estimators relate to linear statistics of a homogeneous Poisson point process (PPP), linear statistics of more regularly spread point processes can yield unbiased estimators with faster‐decaying variance, and thus lower integration ...
Diala Hawat +3 more
wiley +1 more source
Cyclicity in Reproducing Kernel Hilbert Spaces of Analytic Functions [PDF]
Computational Methods and Function Theory, 2014 We introduce a large family of reproducing kernel Hilbert spaces $\mathcal{H} \subset \mbox{Hol}(\mathbb{D})$, which include the classical Dirichlet-type spaces $\mathcal{D}_α$, by requiring normalized monomials to form a Riesz basis for $\mathcal{H}$. Then, after precisely evaluating the $n$-th optimal norm and the $n$-th approximant of $f(z)=1-z$, we
Fricain, Emmanuel +2 more
openaire +4 more sources
Reproducing kernel Hilbert spaces [PDF]
, 2005 Cataloged from PDF version of article.In this thesis we make a survey of the theory of reproducing kernel Hilbert spaces associated with positive definite kernels and we illustrate their applications for interpolation problems of Nevanlinna-Pick type ...
Okutmuştur, Baver
core
Semi reproducing kernel hilbert spaces and mixed precision computation. [PDF]
, 2018 Positive definite and conditionally positive definite functions are widely used in interpolation and smoothing problems, particularly when the data is scattered.
Garing, Ronald
core +1 more source
Kernel VICReg for Self-Supervised Learning in Reproducing Kernel Hilbert Space
Big Data and Cognitive ComputingSelf-supervised learning (SSL) has emerged as a powerful paradigm for representation learning by optimizing geometric objectives, such as invariance to augmentations, variance preservation, and feature decorrelation, without requiring labels.
M. Hadi Sepanj +3 more
doaj +1 more source
Advances in Difference Equations, 2021 This paper deals with the generalized Bagley–Torvik equation based on the concept of the Caputo–Fabrizio fractional derivative using a modified reproducing kernel Hilbert space treatment.
Shatha Hasan +5 more
doaj +1 more source
Reproducing Kernel Hilbert Spaces Over Interval, Circle, and Sphere [PDF]
, 2021 This thesis discusses the problem of regression models in the field of statistics, and introduces the application of mathematical analysis in statistics.
Hsu, Yu-Chi
core
Representing functional data in reproducing Kernel Hilbert Spaces with applications to clustering and classification [PDF]
Functional data are difficult to manage for many traditional statistical techniques given their very high (or intrinsically infinite) dimensionality. The reason is that functional data are essentially functions and most algorithms are designed to work ...
Alberto Muñoz, Javier González
core
Sampling the diagnostic signals. Part 1. Sampling in the reproducing kernel Hilbert space with Shanon kernel [PDF]
, 2007 W pracy przedstawiono matematyczny opis sygnałów diagnostycznych przestrzeni Hilberta oraz sposób konstrukcji tej przestrzeni. Podano teorię jąder reprodukujących w zastosowaniu do próbkowania sygnałów diagnostycznych oraz zapis klasycznego twierdzenia o
Syroka, Z.
core