Results 191 to 200 of about 2,581 (224)

On Reproducing Kernel Hilbert Spaces of Polynomials

Mathematische Nachrichten, 1997
AbstractCertain 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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Which Spaces can be Embedded in Reproducing Kernel Hilbert Spaces?

Constructive Approximation
Given 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, Steinwart Ingo
exaly   +2 more sources

Adaptive estimation in reproducing kernel Hilbert spaces

2017 American Control Conference (ACC), 2017
This 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, Suprotim Majumdar, Savio Pereira, Andrew J. Kurdila, John B. Ferris   +4 more
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The Henderson Smoother in Reproducing Kernel Hilbert Space

Journal of Business & Economic Statistics, 2008
The 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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Reproducing Kernel Hilbert Spaces With Odd Kernels in Price Prediction

IEEE Transactions on Neural Networks and Learning Systems, 2012
For 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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QMC Integration in Reproducing Kernel Hilbert Spaces

2014
We 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, Friedrich Pillichshammer   +1 more
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Reproducing Kernel Hilbert Spaces

2022
Lucas Valentin Umann, Wolfgang Karl Härdle   +1 more
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Skill Disentanglement in Reproducing Kernel Hilbert Space

Proceedings of the AAAI Conference on Artificial Intelligence
Unsupervised Skill Discovery aims at learning diverse skills without any extrinsic rewards and leverage them as prior for learning a variety of downstream tasks. Existing approaches to unsupervised reinforcement learning typically involve discovering skills through empowerment-driven techniques or by maximizing entropy to encourage exploration. However,
Vedant Dave, Elmar Rueckert
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Subspace classifier in reproducing kernel Hilbert space

IJCNN'99. International Joint Conference on Neural Networks. Proceedings (Cat. No.99CH36339), 2003
To improve the performance of subspace classifier, it is effective to reduce the dimensionality of the intersections between subspaces. For this purpose, the feature space is mapped implicitly to a high dimensional reproducing kernel Hilbert space and the subspace classifier is applied in this space.
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Reproducing Kernels of Hilbert Spaces

2025 27th International Symposium on Symbolic and Numeric Algorithms for Scientific Computing (SYNASC)
Diane Guignard, Olivier D. Lafitte
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