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Reproducing kernel Hilbert spaces associated with kernels on topological spaces
Functional Analysis and Its Applications, 2012 We analyze reproducing kernel Hilbert spaces of positive definite kernels on a topological space X being either first countable or locally compact. The results include versions of Mercer's theorem and theorems on the embedding of these spaces into spaces
Valdir Antonio Menegatto +1 more
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Which Spaces can be Embedded in Reproducing Kernel Hilbert Spaces? [PDF]
Constructive ApproximationGiven a Banach space $E$ consisting of functions, we ask whether there exists a reproducing kernel Hilbert space $H$ with bounded kernel such that $E\subset H$.
Ingo Steinwart
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2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2018
Domain shift, which occurs when there is a mismatch between the distributions of training (source) and testing (target) datasets, usually results in poor performance of the trained model on the target domain.
Zhen Zhang +3 more
semanticscholar +1 more source
Domain shift, which occurs when there is a mismatch between the distributions of training (source) and testing (target) datasets, usually results in poor performance of the trained model on the target domain.
Zhen Zhang +3 more
semanticscholar +1 more source
Adaptive estimation in reproducing kernel Hilbert spaces
2017 American Control Conference (ACC), 2017This paper introduces a novel framework for the study of adaptive or online estimation problems for a common class of nonlinear systems governed by ordinary differential equations (ODEs) on ℝd. In contrast to most conventional strategies for ODEs, the approach here embeds the estimate of the unknown nonlinear function appearing in the plant in a ...
Parag Bobade +4 more
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The Henderson Smoother in Reproducing Kernel Hilbert Space
Journal of Business & Economic Statistics, 2008The Henderson smoother has been traditionally applied for trend-cycle estimation in the context of nonparametric seasonal adjustment software officially adopted by statistical agencies. This study introduces a Henderson third-order kernel representation by means of the reproducing kernel Hilbert space (RKHS) methodology.
DAGUM, ESTELLE BEE, BIANCONCINI, SILVIA
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QMC Integration in Reproducing Kernel Hilbert Spaces
2014We return to the problem of numerical integration of multivariate functions. As already mentioned in Sect. 1.1, we normalize the integration domain to be the compact unit cube [0, 1] s , and hence the integrals considered are of the form ( 1.1).
Gunther Leobacher +1 more
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Reproducing Kernel Hilbert Spaces With Odd Kernels in Price Prediction
IEEE Transactions on Neural Networks and Learning Systems, 2012For time series of futures contract prices, the expected price change is modeled conditional on past price changes. The proposed model takes the form of regression in a reproducing kernel Hilbert space with the constraint that the regression function must be odd.
Milos Krejnik, Anton Tyutin
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Adaptive estimation for nonlinear systems using reproducing kernel Hilbert spaces
Advances in Computational Mathematics, 2017This paper extends a conventional, general framework for online adaptive estimation problems for systems governed by unknown or uncertain nonlinear ordinary differential equations. The central feature of the theory introduced in this paper represents the
Parag Bobade +4 more
semanticscholar +1 more source
Multiscale Approximation and Reproducing Kernel Hilbert Space Methods
SIAM Journal on Numerical Analysis, 2015zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Michael Griebel +2 more
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