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This survey is an introduction to positive definite kernels and the set of methods they have inspired in the machine learning literature, namely kernel methods. We first discuss some properties of positive definite kernels as well as…

Machine Learning · Statistics 2009-12-04 Marco Cuturi

In this paper we continue to investigate a certain class of Hankel-like positive definite kernels using their associated orthogonal polynomials. The main result of this paper is about the structure of this kind of kernels.

Functional Analysis · Mathematics 2007-05-23 T. Banks , T. Constantinescu

We define a family of kernels for mixed continuous/discrete hierarchical parameter spaces and show that they are positive definite.

Machine Learning · Statistics 2013-10-23 Frank Hutter , Michael A. Osborne

We give two new global and algorithmic constructions of the reproducing kernel Hilbert space associated to a positive definite kernel. We further present ageneral positive definite kernel setting using bilinear forms, and we provide new…

Functional Analysis · Mathematics 2020-11-20 Daniel Alpay , Palle Jorgensen

We provide a characterization for the continuous positive definite kernels on $\mathbb R^d$ that are invariant to linear isometries, i.e. invariant under the orthogonal group $O(d)$. Furthermore, we provide necessary and sufficient…

Statistics Theory · Mathematics 2025-06-30 Felix Benning , Max David Schölpple

In this paper we investigate the class of invariant positive definite kernels on the free semigroup on N generators. We provide a combinatorial description of the positivity of the kernel in terms of Dyck paths and then we find a…

Functional Analysis · Mathematics 2007-05-23 T. Banks , T. Constantinescu , Nermine El-Sissi

With view to applications in stochastic analysis and geometry, we introduce a new correspondence for positive definite kernels (p.d.) $K$ and their associated reproducing kernel Hilbert spaces. With this we establish two kinds of…

Functional Analysis · Mathematics 2019-11-28 Palle Jorgensen , Feng Tian

We suggest that a certain one-to-one parametrization of completely positive maps on the matrix algebra might be useful in the study of quantum channels. This is illustrated in the case of binary quantum channels. While the algorithm is…

Operator Algebras · Mathematics 2007-05-23 T. Constantinescu

We study classes of reproducing kernels $K$ on general domains; these are kernels which arise commonly in machine learning models; models based on certain families of reproducing kernel Hilbert spaces. They are the positive definite kernels…

Functional Analysis · Mathematics 2017-08-22 Palle Jorgensen , Feng Tian

We consider a kernel based harmonic analysis of "boundary," and boundary representations. Our setting is general: certain classes of positive definite kernels. Our theorems extend (and are motivated by) results and notions from classical…

Functional Analysis · Mathematics 2016-11-15 Palle Jorgensen , Feng Tian

We present new classes of positive definite kernels on non-standard spaces that are integrally strictly positive definite or characteristic. In particular, we discuss radial kernels on separable Hilbert spaces, and introduce broad classes…

Machine Learning · Statistics 2022-06-16 Johanna Ziegel , David Ginsbourger , Lutz Dümbgen

We review machine learning methods employing positive definite kernels. These methods formulate learning and estimation problems in a reproducing kernel Hilbert space (RKHS) of functions defined on the data domain, expanded in terms of a…

Statistics Theory · Mathematics 2009-09-29 Thomas Hofmann , Bernhard Schölkopf , Alexander J. Smola

In this paper we show how specific families of positive definite kernels serve as powerful tools in analyses of iteration algorithms for multiple layer feedforward Neural Network models. Our focus is on particular kernels that adapt well to…

Machine Learning · Computer Science 2023-01-09 Palle E. T. Jorgensen , Myung-Sin Song , James Tian

This work explores new classes of nonstationary stochastic sequences associated with polynomial hypergroups. Their covariance structures are analyzed through positive definite kernels and corresponding Hilbert spaces. Novel consistent…

Functional Analysis · Mathematics 2024-11-27 Volker Hösel

The paper studies strictly positive definite kernels on compact Riemannian manifolds. We state new conditions to ensure strict positive definiteness for general kernels and kernels with certain convolutional structure. We also state…

Numerical Analysis · Mathematics 2023-01-20 Jean Carlo Guella , Janin Jäger

We present a characterization for a positive definite operator valued kernel to be universal or $C_{0}$-universal, and apply these characterizations to a family of operator valued kernels that are shown to be well behaved. Later, we obtain…

Functional Analysis · Mathematics 2020-03-26 Jean Carlo Guella

We study positive definite kernels pulled back along a finite family of self-maps under a subinvariance inequality for the associated branching operator. Iteration produces an increasing kernel tower with defect kernels. Under diagonal…

Probability · Mathematics 2026-02-03 James Tian

We introduce the notion of multiplication kernels of birational and $D$-module type and give various examples. We also introduce the notion of a semi-classical multiplication kernel associated with an integrable system and discuss its…

Algebraic Geometry · Mathematics 2022-01-05 Maxim Kontsevich , Alexander Odesskii

For the real, continuous, isotropic and positive definite kernels on a product of spheres, one may consider not only its usual strict positive definiteness but also strict positive definiteness restrict to the points of the product that…

Classical Analysis and ODEs · Mathematics 2015-05-15 Jean C. Guella , Valdir A. Menegatto

We study positive definiteness of kernels $K(x,y)$ on two-point homogeneous spaces. As opposed to the classical case, which has been developed and studied in the existing literature, we allow the kernel to have an (integrable) singularity…

Classical Analysis and ODEs · Mathematics 2024-10-30 Dmitriy Bilyk , Peter Grabner
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