Results 11 to 20 of about 5,834,158 (292)
Convolutional kernel function algebra
Frontiers in Computer Science, 2022 Many systems for image manipulation, signal analysis, machine learning, and scientific computing make use of discrete convolutional filters that are known before computation begins.
Edward Stow, Paul H. J. Kelly
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Transformation kernel density estimation of actuarial loss functions [PDF]
, 2013 [cat] Es presenta un estimador nucli transformat que és adequat per a distribucions de cua pesada. Utilitzant una transformació basada en la distribució de probabilitat Beta l’elecció del paràmetre de finestra és molt directa. Es presenta una aplicació a
Guillén, Montserrat +2 more
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The determination of the poles of the mapping function and their use in numerical conformal mapping [PDF]
, 1982 Let f be the function which maps conformally a simply-connected domain Ω onto the unit disc. This paper is concerned with the problem of determining the dominant poles of f in comp1(Ω∩∂Ω), and of using this information in order to obtain accurate ...
Warby, MK, Hough, DM, Papamichael, N
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Maximum kernel likelihood estimation [PDF]
, 2008 We introduce an estimator for the population mean based on maximizing likelihoods formed by parameterizing a kernel density estimate. Due to these origins, we have dubbed the estimator the maximum kernel likelihood estimate (mkle). A speedy computational
Jaki, Thomas, West, R. Webster
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Developing a Strategy for Buying and Selling Stocks Based on Semi-Parametric Markov Switching Time Series Models [PDF]
Iranian Journal of Finance, 2021 The modeling of strategies for buying and selling in Stock Market Investment has been the object of numerous advances and uses in economic studies, both theoretically and empirically.
Hossein Naderi +3 more
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Averaging of kernel functions [PDF]
Neurocomputing, 2013 In kernel-based machines, the integration of a number of different kernels to build more flexible learning methods is a promising avenue for research. In multiple kernel learning, a compound kernel is build by learning a kernel that is a positively weighted arithmetic mean of several sources.
Lluís A. Belanche Muñoz +1 more
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A Theorem for Kernel Functions [PDF]
Proceedings of the American Mathematical Society, 1951 Let B be a domain lying in the complex z plane and KB(Z, i) its kernel function. A number of relationships exist between the kernel and the geometric properties of the domain. (See, for example, [I'].)i It is the purpose of the-present note to relate the successive derivatives of the kernel with the domain B.
Davis, Philip, Pollak, Henry
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Studies of different kernel functions in nuclear mass predictions with kernel ridge regression
Frontiers in Physics, 2023 The kernel ridge regression (KRR) approach has been successfully applied in nuclear mass predictions. Kernel function plays an important role in the KRR approach.
X. H. Wu
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Weighted p-norm distance t kernel SVM classification algorithm based on improved polarization
Scientific Reports, 2022 The kernel function in SVM enables linear segmentation in a feature space for a large number of linear inseparable data. The kernel function that is selected directly affects the classification performance of SVM.
Wenbo Liu, Shengnan Liang, Xiwen Qin
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The harmonic analysis of kernel functions [PDF]
Automatica, 2018 Kernel-based methods have been recently introduced for linear system identification as an alternative to parametric prediction error methods. Adopting the Bayesian perspective, the impulse response is modeled as a non-stationary Gaussian process with zero mean and with a certain kernel (i.e. covariance) function.
Mattia Zorzi, Alessandro Chiuso
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