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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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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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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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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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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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Gazi Üniversitesi Fen Bilimleri Dergisi, 2021 Weirs are the oldest and most practical structures used to control, regulate, and measure flow in rivers or open channels. The ratio of the actual discharge, smaller than the theoretical discharge due to the separation zone and boundary layer development,
Oğuz ŞİMŞEK +2 more
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International Journal of Advanced Robotic Systems, 2020 The multivariate statistical method such as principal component analysis based on linear dimension reduction and kernel principal component analysis based on nonlinear dimension reduction as the modified principal component analysis method are commonly ...
Liming Li +3 more
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Short-Term Load Forecasting Based on Integration of SVR and Stacking
IEEE Access, 2020 Selection of the kernel function by the support vector regression (SVR), for the purposes of load forecasting, is affected by the power load characteristics.
Zhenqi Tan +5 more
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