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- W2897432128 abstract "Abstract Kernel-based regularization has recently been shown to be a successful method for impulse response estimation. This technique usually requires choosing a vector of hyper-parameters in order to form an appropriate regularization matrix. In this paper, we develop an alternative way to obtain kernel-based regularization estimates by Bayesian model mixing. This new approach is tested against state-of-the-art methods for hyperparameter tuning in regularized FIR estimation, with favorable results in many cases." @default.
- W2897432128 created "2018-10-26" @default.
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- W2897432128 date "2018-01-01" @default.
- W2897432128 modified "2023-10-16" @default.
- W2897432128 title "A fully Bayesian approach to kernel-based regularization for impulse response estimation" @default.
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- W2897432128 doi "https://doi.org/10.1016/j.ifacol.2018.09.123" @default.
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