Results 111 to 120 of about 14,885,883 (209)

The theory and application of penalized methods or Reproducing Kernel Hilbert Spaces made easy

Statistics Surveys, 2012
The popular cubic smoothing spline estimate of a regression function arises as the minimizer of the penalized sum of squares $\sum_j(Y_j - μ(t_j))^2 + λ\int_a^b [μ"(t)]^2 dt$, where the data are $t_j,Y_j$, $j=1,..., n$. The minimization is taken over an infinite-dimensional function space, the space of all functions with square integrable second ...
openaire   +3 more sources

Applying the possibilistic C-means algorithm in kernel-induced spaces [PDF]

, 2010
In this paper, we study a kernel extension of the classic possibilistic c-means. In the proposed extension, we implicitly map input patterns into a possibly high-dimensional space by means of positive semidefinite kernels. In this new space, we model the
Masulli, F., M. Filippone, Filippone, M., ROVETTA, STEFANO, MASULLI, FRANCESCO, Rovetta, S.   +5 more
core   +1 more source

Data-driven Nonlinear System Identification Under Bounded Noise Using Kernel Learning-based Ellipsoidal Set-membership

Journal of Telecommunications and Information Technology
Nonlinear system identification under bounded interferences remains a challenging problem when the noise bound is unavailable a priori. This paper presents a recursive kernel-based set-membership identification algorithm that combines the ellipsoidal ...
Hasna El Maizi, Mathieu Pouliquen, Rachid Fateh, Miloud Frikel, Said Safi   +4 more
doaj   +1 more source

Reproducing kernel Hilbert spaces of Gaussian priors

, 2008
We review definitions and properties of reproducing kernel Hilbert spaces attached to Gaussian variables and processes, with a view to applications in nonparametric Bayesian statistics using Gaussian priors.
Vaart, AW Aad van der, A. W. van der Vaart, van der Vaart, A.W., Vaart, van der, A.W., van Zanten, J.H., Zanten, van, J.H., Zanten, JH Harry van, J. H. van Zanten   +7 more
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Numerical Simulation of the Kudryashov–Sinelshchikov Equation for Modeling Pressure Waves in Liquids with Gas Bubbles

Mathematics
The Kudryashov–Sinelshchikov equation (KSE) is crucial in modeling pressure waves in liquids containing gas bubbles, capturing both nonlinear wave phenomena and dispersion effects.
Gayatri Das, Bibekananda Sitha, Rajesh Kumar Mohapatra, Predrag Stanimirović, Tzung-Pei Hong   +4 more
doaj   +1 more source

REPRODUCING KERNEL HILBERT SPACE METHOD]{REPRESENTATION FOR THE REPRODUCING KERNEL HILBERT SPACE METHOD FOR A NONLINEAR SYSTEM

Hacettepe Journal of Mathematics and Statistics, 2018
Esra Karataş Akgül, Ali Akgül, Yasir Khan, Dumitru Baleanu   +3 more
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Reproducing kernel Hilbert spaces [PDF]

, 2005
Cataloged from PDF version of article.In this thesis we make a survey of the theory of reproducing kernel Hilbert spaces associated with positive definite kernels and we illustrate their applications for interpolation problems of Nevanlinna-Pick type ...
Okutmuştur, Baver
core  

The method of Successive Approximations for Reproducing Kernel Hilbert Spaces (1)

, 2001
A general framework for function approximation from finite data is presented based on reproducing kernel Hilbert spaces. Key results are summarised and the normal and regularised solutions are described. A potential limitation to these solutions for large data sets is the computational burden.
Dodd, T.J., Harrison, R.F.
openaire  

Reproducing kernel hilbert space methods for cad tools [PDF]

, 2004
The review of known RKHS-methods for analysis of current state in science investigations is represented. The place of Series Summation Method in Reproducing Kernel Hilbert Space (RKHS) is determined. The new results obtained by this method are discussed.
Gowher, Malik, Chumachenko, S. V., Khawar, Parvez   +2 more
core  

Representation for the reproducing kernel Hilbert space method for a nonlinear system

, 2017
We apply the reproducing kernel Hilbert space method to a nonlinear system in this work. We utilize this technique to overcome the nonlinearity of the problem. We obtain accurate results. We demonstrate our results by tables and figures. We prove the efficiency of the method.
KARATAS AKGÜL, Esra, AKGÜL, Ali, KHAN, Yasir, BALEANU, Dumitru   +3 more
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