Results 31 to 40 of about 871 (187)
Error Bound of Mode-Based Additive Models
Entropy, 2021 Due to their flexibility and interpretability, additive models are powerful tools for high-dimensional mean regression and variable selection. However, the least-squares loss-based mean regression models suffer from sensitivity to non-Gaussian noises ...
Hao Deng +3 more
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Operational reproducing kernel Hilbert spaces
Lithuanian Mathematical Journal, 1972 The abstracts (in two languages) can be found in the pdf file of the article. Original author name(s) and title in Russian and Lithuanian: Э. Сенкене, А. Темпельман. Гильбертовы пространства с операторными воспроизводящими ядрами E. Senkienė, A. Tempelmanas.
E. Senkienė, A. Tempelman
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The Convergence Rate for a K-Functional in Learning Theory
Journal of Inequalities and Applications, 2010 It is known that in the field of learning theory based on reproducing kernel Hilbert spaces the upper bounds estimate for a K-functional is needed.
Bao-Huai Sheng, Dao-Hong Xiang
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Advanced Intelligent Discovery, EarlyView.Feature selection combined with machine learning and high‐throughput experimentation enables efficient handling of high‐dimensional datasets in emerging photovoltaics. This approach accelerates material discovery, improves process optimization, and strengthens stability prediction, while overcoming challenges in data quality and model scalability to ...
Jiyun Zhang +5 more
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Some Notes on Error Analysis for Kernel Based Regularized Interpolation
International Journal of Analysis and Applications, 2020 Kernel based regularized interpolation is one of the most important methods for approximating functions. The theory behind the kernel based regularized interpolation is the well-known Representer Theorem, which shows the form of approximation function in
Qing Zou
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Alexandria Engineering Journal, 2021 In this paper, a coupled system of fractional differential equations along with integral boundary conditions is discussed by means of the iterative reproducing kernel algorithm.
Rania Saadeh
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InfoMat, EarlyView.To address the limitations of conventional trial‐and‐error approaches, perovskite solar cell research is shifting toward a new paradigm that utilizes datasets and AI. This review examines the fundamental elements of data‐driven and AI‐integrated research: data platforms, AI methodologies, and self‐driving laboratories, demonstrating how their ...
Jaehee Lee +5 more
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Reproducing Kernel Hilbert Space and Coalescence Hidden-variable Fractal Interpolation Functions
Demonstratio Mathematica, 2019 Reproducing Kernel Hilbert Spaces (RKHS) and their kernel are important tools which have been found to be incredibly useful in many areas like machine learning, complex analysis, probability theory, group representation theory and the theory of integral ...
Prasad Srijanani Anurag
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Mathematical Methods in the Applied Sciences, EarlyView.A highly accurate numerical method is given for the solution of boundary value problem of generalized Bagley‐Torvik (BgT) equation with Caputo derivative of order 0<β<2$$ 0<\beta <2 $$ by using the collocation‐shooting method (C‐SM). The collocation solution is constructed in the space Sm+1(1)$$ {S}_{m+1}^{(1)} $$ as piecewise polynomials of degree at ...
Suzan Cival Buranay +2 more
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Operator inequalities in reproducing kernel Hilbert spaces
Communications Faculty Of Science University of Ankara Series A1Mathematics and Statistics, 2022 Summary: In this paper, by using some classical Mulholland type inequality, Berezin symbols and reproducing kernel technique, we prove the power inequalities for the Berezin number \(\operatorname{ber}(A)\) for some self-adjoint operators \(A\) on \({H}(\Omega)\).
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