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• W3109867339 abstract "Multi-kernel based methods have better and more flexible performance due to more freedom degrees and united features than the mono-kernel methods. In this paper, we present the random Fourier multi-kernel least-mean-square (RFF-MKLMS) algorithm, and derive its analytical models in the mean and mean-square error sense to characterize its transient and steady-state stochastic behaviors. The theoretical predictions consistently match with the simulated learning curves during the transient and steady-state phases, which validate the accuracy of theoretical findings." @default.
• W3109867339 created "2020-12-07" @default.
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• W3109867339 date "2020-08-21" @default.
• W3109867339 modified "2023-09-25" @default.
• W3109867339 title "Random Fourier Features Multi-Kernel LMS Algorithm" @default.
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• W3109867339 doi "https://doi.org/10.1109/icspcc50002.2020.9259504" @default.
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