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Explicit recursivity into reproducing kernel Hilbert spaces
2011 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2011 This paper presents a methodology to develop recursive filters in reproducing kernel Hilbert spaces (RKHS). Unlike previous approaches that exploit the kernel trick on filtered and then mapped samples, we explicitly define model recursivity in the Hilbert space. The method exploits some properties of functional analysis and recursive computation of dot
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An Explicit Description of the Reproducing Kernel Hilbert Spaces of Gaussian RBF Kernels
IEEE Transactions on Information Theory, 2006Ingo Steinwart, D. Hush, C. Scovel
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Berezin number inequalities of operators on reproducing kernel Hilbert spaces
Rocky Mountain Journal of Mathematics, 2022Several new upper bounds for the Berezin number of bounded linear operators defined on reproducing kernel Hilbert spaces are given. The bounds obtained here improve on the earlier ones.
Anirban Sen, Pintu Bhunia, K. Paul
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Quantile Regression in Reproducing Kernel Hilbert Spaces
Journal of the American Statistical Association, 2007Yufeng Liu, Ji Zhu
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Regularization in a functional reproducing kernel Hilbert space
Journal of Complexity, 2021zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Rui Wang 0096, Yuesheng Xu
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Sampling Expansions in Reproducing Kernel Hilbert and Banach Spaces
Numerical Functional Analysis and Optimization, 2009 Given a countable subset of a set , we investigate the construction of all the reproducing kernel Hilbert (resp. Banach) spaces on that have as a sampling (resp. p-sampling) set.
Qiyu Sun, Deguang Han, M Z Nashed
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Orthogonality from disjoint support in reproducing kernel Hilbert spaces [PDF]
Journal of Mathematical Analysis and Applications, 2009 We investigate reproducing kernel Hilbert spaces (RKHS) where two functions are orthogonal whenever they have disjoint support. Necessary and sufficient conditions in terms of feature maps for the reproducing kernel are established.
Haizhang Zhang
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Partially functional linear regression in reproducing kernel Hilbert spaces
Computational Statistics & Data Analysis, 2020In this paper, we study the partially functional linear regression model in which there are both functional predictors and traditional multivariate predictors.
Xia Cui, Hongmei Lin, Heng Lian
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Analysis and Applications
Commonly used $f$-divergences of measures, e.g., the Kullback-Leibler divergence, are subject to limitations regarding the support of the involved measures.
Viktor Stein +3 more
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Commonly used $f$-divergences of measures, e.g., the Kullback-Leibler divergence, are subject to limitations regarding the support of the involved measures.
Viktor Stein +3 more
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On Reproducing Kernel Hilbert Spaces of Polynomials
Mathematische Nachrichten, 1997AbstractCertain Hilbert spaces of polynomials, called Szegö spaces [11], are studied. A transformation, called Hilbert traneformation, is constructed for every polynomial associatted with a Szegö space. An orthogonal set is found in a Szegö space which determines the norm of the space. A matrix factorization theory is obtained for defining polynomials.
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