Results 61 to 70 of about 11,485 (217)
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 $.
Li He
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Advanced Quantum Technologies, EarlyView.The hybrid approach to Quantum Supervised Machine Learning is compatible with Noisy Intermediate Scale Quantum (NISQ) devices but hardly useful. Pure quantum kernels requiring fault‐tolerant quantum computers are more promising. Examples are kernels computed by means of the Quantum Fourier Transform (QFT) and kernels defined via the calculation of ...
Massimiliano Incudini +2 more
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The Feichtinger Conjecture and Reproducing Kernel Hilbert Spaces [PDF]
Indiana University Mathematics Journal, 2011 15 ...
Vern I. Paulsen, Sneh Lata
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Abstract and Applied Analysis, 2014 A numerical method based on the reproducing kernel theorem is presented for the numerical solution of a three-point boundary value problem with an integral condition.
Jing Niu, Ping Li
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Statistical Complexity of Quantum Learning
Advanced Quantum Technologies, EarlyView.The statistical performance of quantum learning is investigated as a function of the number of training data N$N$, and of the number of copies available for each quantum state in the training and testing data sets, respectively S$S$ and V$V$. Indeed, the biggest difference in quantum learning comes from the destructive nature of quantum measurements ...
Leonardo Banchi +3 more
wiley +1 more source
VARIOUS INEQUALITIES IN REPRODUCING KERNEL HILBERT SPACES [PDF]
Taiwanese Journal of Mathematics, 2013 In this paper, we examine various reproducing kernel Hilbert spaces $\mathcal{H}_{K_1}$ and $\mathcal{H}_{K_2}$ such that the inequality \[\det \left[\langle F_iG_i,F_jG_j\rangle_{\mathcal{H}_{K_1K_2}}\right]_{i,j=1}^m\le C\det\left[\langle F_i,F_j\rangle_{\mathcal{H}_{K_1}}\langle G_i,G_j\rangle_{\mathcal{H}_{K_2}} \right]_{i,j=1}^m\]holds for all ...
Nhan, Nguyen Du Vi, Duc, Dinh Thanh
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Density Problem and Approximation Error in Learning Theory
Abstract and Applied Analysis, 2013 We study the density problem and approximation error of reproducing kernel Hilbert spaces for the purpose of learning theory. For a Mercer kernel on a compact metric space (, ), a characterization for the generated reproducing kernel Hilbert space (RKHS)
Ding-Xuan Zhou
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Combining kernelised autoencoding and centroid prediction for dynamic multi‐objective optimisation
CAAI Transactions on Intelligence Technology, EarlyView.Abstract Evolutionary algorithms face significant challenges when dealing with dynamic multi‐objective optimisation because Pareto optimal solutions and/or Pareto optimal fronts change. The authors propose a unified paradigm, which combines the kernelised autoncoding evolutionary search and the centroid‐based prediction (denoted by KAEP), for solving ...
Zhanglu Hou +4 more
wiley +1 more source
Operator inequalities in reproducing kernel Hilbert spaces
Communications Faculty Of Science University of Ankara Series A1Mathematics and Statistics, 2022 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 ber(A) for some self-adjoint operators A on H(Omega). Namely, some Mulholland type inequality for reproducing kernel Hilbert space operators are established.
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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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