Results 81 to 90 of about 1,714 (187)

Application of Reproducing Kernel Method for Solving Nonlinear Fredholm-Volterra Integrodifferential Equations

Abstract and Applied Analysis, 2012
This paper investigates the numerical solution of nonlinear Fredholm-Volterra integro-differential equations using reproducing kernel Hilbert space method. The solution 𝑢(𝑥) is represented in the form of series in the reproducing kernel space.
Omar Abu Arqub, Mohammed Al-Smadi, Shaher Momani   +2 more
doaj   +1 more source

A Characterization for reproducing kernel Hilbert spaces

Journal of Mathematical Analysis and Applications, 1974
AbstractLet G(t, s) be the Green's functions associated with N, a differential operator restricted to certain boundary conditions. Define (u, v)N = (Nu, v)L2. It is shown that the reproducing kernel Hilbert space generated by G is the same as the Hilbert-space completion with respect to ∥ · ∥N of the set of real valued functions which are in C2n and ...
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Flexible Expectile Regression in Reproducing Kernel Hilbert Spaces

Technometrics, 2017
Expectile, first introduced by Newey and Powell in 1987 in the econometrics literature, has recently become increasingly popular in risk management and capital allocation for financial institutions due to its desirable properties such as coherence and elicitability.
Yang, Yi, Zhang, Teng, Zou, Hui
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Learning Rate of Regularized Regression Associated with Zonal Translation Networks

Mathematics
We give a systematic investigation on the reproducing property of the zonal translation network and apply this property to kernel regularized regression.
Xuexue Ran, Baohuai Sheng, Shuhua Wang
doaj   +1 more source

High-Order Sequential Simulation via Statistical Learning in Reproducing Kernel Hilbert Space. [PDF]

Math Geosci, 2020
Yao L, Dimitrakopoulos R, Gamache M.
europepmc   +1 more source

Kernel least absolute shrinkage and selection operator regression classifier for pattern classification

IET Computer Vision, 2013
The feature vectors in feature space are more likely to be linearly separable than the observations in input space. To enhance the separability of the feature vectors, the authors perform least absolute shrinkage and selection operator (LASSO) regression
Jie Xu, Jun Yin
doaj   +1 more source

Some Lemmas on Reproducing Kernel Hilbert Spaces [PDF]

, 2002
Reproducing kernel Hilbert spaces (RKHS) provides a framework for approximation from finite data using the idea of bounded linear functionals. The approximation problem in this case can be viewed as the inverse problem of finding the optimum operator from the Euclidean space of observations to some subspace of the RKHS.
Dodd, T.J., Harrison, R.F.
openaire  

Neighborhood preserving sparse representation based on Nyström method for image set classification on symmetric positive definite matrices

Journal of Algorithms & Computational Technology, 2019
In the field of pattern recognition, using the symmetric positive-definite matrices to represent image set has been widely studied, and sparse representation-based classification algorithm on the symmetric positive-definite matrix manifold has attracted ...
Chu Li, Xiao-Jun Wu
doaj   +1 more source

Reproducing kernel Hilbert spaces

, 2005
105
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Optimal Penalized Function-on-Function Regression under a Reproducing Kernel Hilbert Space Framework. [PDF]

J Am Stat Assoc, 2018
Sun X, Du P, Wang X, Ma P.
europepmc   +1 more source