Results 31 to 40 of about 33,561,195 (290)
Error estimation for the reproducing kernel method to solve linear boundary value problems
Journal of Computational and Applied Mathematics, 2013 B Y Wu
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Bernoulli, 2022 This paper proposes and studies a numerical method for approximation of posterior expectations based on interpolation with a Stein reproducing kernel. Finite-sample-size bounds on the approximation error are established for posterior distributions supported on a compact Riemannian manifold, and we relate these to a kernel Stein discrepancy (KSD ...
Barp A +3 more
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Modified reproducing kernel method for singularly perturbed boundary value problems with a delay
Applied Mathematical Modelling, 2015 F Z Geng
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A Kernel Method for Classification [PDF]
, 2004 Kernel Maximum Likelihood Hebbian Learning Scale Invariant Maps is a novel technique developed to facilitate the clustering of complex data effectively and efficiently and that is characterised for converging remarkably quickly. The combination of Maximum Likelihood Hebbian Learning Scale Invariant Map and the Kernel Space provides a very smooth scale ...
MacDonald, Donald +4 more
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Instrumental variable regression via kernel maximum moment loss
Journal of Causal Inference, 2023 We investigate a simple objective for nonlinear instrumental variable (IV) regression based on a kernelized conditional moment restriction known as a maximum moment restriction (MMR).
Zhang Rui +3 more
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Free-Breathing and Ungated Cardiac MRI Reconstruction Using a Deep Kernel Representation
Applied Sciences, 2023 Free-breathing and ungated cardiac MRI is a challenging problem due to the cardiac motion and respiration motion, which are not tracked. In this work, we propose an unsupervised deep kernel method for reconstructing real-time free-breathing and ungated ...
Qing Zou +3 more
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A compressive multi-kernel method for privacy-preserving machine learning [PDF]
IEEE International Joint Conference on Neural Network, 2017 As the analytic tools become more powerful, and more data are generated on a daily basis, the issue of data privacy arises. This leads to the study of the design of privacy-preserving machine learning algorithms.
Thee Chanyaswad, J. M. Chang, S. Kung
semanticscholar +1 more source
Information Theory With Kernel Methods
IEEE Transactions on Information Theory, 2023 We consider the analysis of probability distributions through their associated covariance operators from reproducing kernel Hilbert spaces. We show that the von Neumann entropy and relative entropy of these operators are intimately related to the usual notions of Shannon entropy and relative entropy, and share many of their properties.
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A More Efficient and Practical Modified Nyström Method
Mathematics, 2023 In this paper, we propose an efficient Nyström method with theoretical and empirical guarantees. In parallel computing environments and for sparse input kernel matrices, our algorithm can have computation efficiency comparable to the conventional Nyström
Wei Zhang +3 more
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Mathematics, 2022 For a fixed time, t, and a horizon time, b, the probability of default (PD) measures the probability that an obligor, that has paid his/her credit until time t, runs into arrears not later that time t+b.
Rebeca Peláez +2 more
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