Results 51 to 60 of about 21,130 (212)
Signal-to-noise ratio in reproducing kernel Hilbert spaces [PDF]
Pattern Recognition Letters, 2018 This paper introduces the kernel signal-to-noise ratio (kSNR) for different machine learning and signal processing applications}. The kSNR seeks to maximize the signal variance while minimizing the estimated noise variance explicitly in a reproducing kernel Hilbert space (rkHs).
Gómez-Chova, Luis +2 more
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Big Data and AI‐Powered Modeling: A Pathway to Sustainable Precision Animal Nutrition
Advanced Science, EarlyView.This review summarizes the current landscape of big data and AI‐powered modeling in animal nutrition, covering techniques including intelligent data acquisition, data augmentation, explainable machine learning, heuristic algorithms, and life cycle assessment‐based sustainability evaluation.
Shuai Zhang +3 more
wiley +1 more source
Symmetric Operators and Reproducing Kernel Hilbert Spaces [PDF]
Complex Analysis and Operator Theory, 2009 We establish the following sufficient operator-theoretic condition for a subspace $${S \subset L^2 (\mathbb{R}, d\nu)}$$ to be a reproducing kernel Hilbert space with the Kramer sampling property. If the compression of the unitary group U(t) := e
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Advanced Energy Materials, EarlyView.This perspective highlights how machine learning accelerates sustainable energy materials discovery by integrating quantum‐accurate interatomic potentials with property prediction frameworks. The evolution from statistical methods to physics‐informed neural networks is examined, showcasing applications across batteries, catalysts, and photovoltaics ...
Kwang S. Kim
wiley +1 more source
Toward efficient quantum computation of molecular ground‐state energies
AIChE Journal, EarlyView.Abstract Variational quantum eigensolvers (VQEs) represent a promising approach to computing molecular ground states and energies on modern quantum computers. These approaches use a classical computer to optimize the parameters of a trial wave function, while the quantum computer simulates the energy by preparing and measuring a set of bitstring ...
Farshud Sorourifar +8 more
wiley +1 more source
soft sets, soft rough sets, soft pre-rough sets, information system, decision making
AIMS Mathematics, 2023 For any real $ \beta $ let $ H^2_\beta $ be the Hardy-Sobolev space on the unit disc $ {\mathbb D} $. $ H^2_\beta $ is a reproducing kernel Hilbert space and its reproducing kernel is bounded when $ \beta > 1/2 $.
Li He
doaj +1 more source
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
wiley +1 more source
Boundary Value ProblemsIn this work, we investigate the Klein–Gordon equation, a physical problem, using the reproducing kernel Hilbert space method (RKHSM). The analytical solution is expressed as a series within the reproducing kernel Hilbert space (RKHS).
Hadjer Zerouali +6 more
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Operator Positivity and Analytic Models of Commuting Tuples of Operators
, 2016 We study analytic models of operators of class $C_{\cdot 0}$ with natural positivity assumptions. In particular, we prove that for an $m$-hypercontraction $T \in C_{\cdot 0}$ on a Hilbert space $\mathcal{H}$, there exists a Hilbert space $\mathcal{E ...
Bhattacharjee, Monojit, Sarkar, Jaydeb
core +1 more source
Reproducing kernel Hilbert spaces of Gaussian priors
, 2008 Published in at http://dx.doi.org/10.1214/074921708000000156 the IMS Collections (http://www.imstat.org/publications/imscollections.htm) by the Institute of Mathematical Statistics (http://www.imstat.org)
van der Vaart, A. W., van Zanten, J. H.
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