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In this paper, we reduce the general linear integral equation of the third kind in $L^2(Y,\mu)$, with largely arbitrary kernel and coefficient, to an equivalent integral equation either of the second kind or of the first kind in…

Spectral Theory · Mathematics 2012-10-04 Igor M. Novitskii

Kernels are powerful and versatile tools in machine learning and statistics. Although the notion of universal kernels and characteristic kernels has been studied, kernel selection still greatly influences the empirical performance. While…

Machine Learning · Statistics 2019-02-28 Chun-Liang Li , Wei-Cheng Chang , Youssef Mroueh , Yiming Yang , Barnabás Póczos

Algebraic integrability of the elliptic Calogero-Moser quantum problem related to the deformed root systems A_2(2) is proved. Explicit formulae for integrals are found.

Mathematical Physics · Physics 2007-05-23 L. A. Khodarinova , I. A. Prikhodsky

In this paper we characterise the optimal pointwise size and regularity estimates for the Dunkl Riesz transform kernel involving both the Euclidean metric and the Dunkl metric, where these two metrics are not equivalent. We further…

Classical Analysis and ODEs · Mathematics 2024-02-06 Yongsheng Han , Ming-Yi Lee , Ji Li , Brett D. Wick

This paper investigates the eigenvalue problem of integral operators whose kernels can be expressed as a finite sum of pairwise products of single-variable functions, making them separable. By consdiering the matrix form of the separable…

Functional Analysis · Mathematics 2025-11-20 Soma Hirai , Ryoto Watanabe , Yuki Nishida , Masashi Iwasaki

We consider diffusive systems, regarded as input/output systems with a kernel given as the Fourier--Borel transform of a measure in the left half-plane. Associated with these are a family of weighted Hankel integral operators, and we…

Functional Analysis · Mathematics 2017-04-04 Aolo Bashar Abusaksaka , Jonathan R. Partington

We explore the equivalence between neural networks and kernel methods by deriving the first exact representation of any finite-size parametric classification model trained with gradient descent as a kernel machine. We compare our exact…

Machine Learning · Computer Science 2023-08-10 Brian Bell , Michael Geyer , David Glickenstein , Amanda Fernandez , Juston Moore

Helmke et al. have recently given a formula for the number of reachable pairs of matrices over a finite field. We give a new and elementary proof of the same formula by solving the equivalent problem of determining the number of so called…

Combinatorics · Mathematics 2016-03-18 Samrith Ram

In the paper we consider self-adjoint partial integral operators of Fredholm type $T$ with a degenerate kernel on the space $L_2([a,b]\times[c,d]).$ Essential and discrete spectra of $T$ are described.

Functional Analysis · Mathematics 2015-04-13 Y. K. Eshkavilov , G. P. Arzikulov , F. H. Haydarov

A unified approach is given to kernel functions which intertwine Ruijsenaars difference operators of type A and of type BC. As an application of the trigonometric cases, new explicit formulas for Koornwinder polynomials attached to single…

Quantum Algebra · Mathematics 2009-05-17 Yasushi Komori , Masatoshi Noumi , Jun'ichi Shiraishi

The structure of orthogonal polynomials on $\mathbb{R}^{2}$ with the weight function $| x_{1}^{2}-x_{2}^{2}|^{2k_{0}}| x_{1}x_{2}|^{2k_{1}}e^{-(x_{1}^{2}+x_{2}^{2})/2}$ is based on the Dunkl operators of type $B_{2}$. This refers to the…

Classical Analysis and ODEs · Mathematics 2013-01-31 Charles F. Dunkl

We produce precise estimates for the Kogbetliantz kernel for the approximation of functions on the sphere. Furthermore, we propose and study a new approximation kernel, which has slightly better properties.

Classical Analysis and ODEs · Mathematics 2017-06-30 Peter J. Grabner

In the paper we prove integral formulae for a Riemannian manifold endowed with $k>2$ orthogonal complementary distributions, which generalize well-known formula for $k=2$ and give applications to splitting and isometric immersions of…

Differential Geometry · Mathematics 2020-08-31 Vladimir Rovenski

High-dimensional data in the form of tensors are challenging for kernel classification methods. To both reduce the computational complexity and extract informative features, kernels based on low-rank tensor decompositions have been…

Machine Learning · Statistics 2023-02-17 Kirandeep Kour , Sergey Dolgov , Peter Benner , Martin Stoll , Max Pfeffer

Kernel matrices are a key quantity in kernel-based approximation, and important properties such as stability and algorithmic convergence can be analyzed with their help. In this work we refine a multivariate Ingham-type theorem, which is…

Numerical Analysis · Mathematics 2025-07-28 Tizan Wenzel , Armin Iske

We introduce kernel thinning, a new procedure for compressing a distribution $\mathbb{P}$ more effectively than i.i.d. sampling or standard thinning. Given a suitable reproducing kernel $\mathbf{k}_{\star}$ and $O(n^2)$ time, kernel…

Machine Learning · Statistics 2024-05-14 Raaz Dwivedi , Lester Mackey

Discrete spectral transformations of skew orthogonal polynomials are presented. From these spectral transformations, it is shown that the corresponding discrete integrable systems are derived both in 1+1 dimension and in 2+1 dimension.…

Mathematical Physics · Physics 2012-03-01 Hiroshi Miki , Hiroaki Goda , Satoshi Tsujimoto

A differential algebra of finite type over a field k is a filtered algebra A, such that the associated graded algebra is finite over its center, and the center is a finitely generated k-algebra. The prototypical example is the algebra of…

Rings and Algebras · Mathematics 2007-05-23 Amnon Yekutieli , James J. Zhang

H\"older estimates and Harnack inequalities are studied for fully nonlinear integro-differential equations under some mild assumptions. We allow the kernels of variable order and critically close to 2.

Analysis of PDEs · Mathematics 2022-07-07 Shuhei Kitano

Kernel methods play an important role in machine learning applications due to their conceptual simplicity and superior performance on numerous machine learning tasks. Expressivity of a machine learning model, referring to the ability of the…

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