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- W4313203592 abstract "Gaussian process regression (GPR) has been a popular Bayesian method for nonlinear fitting. It has the advantage at predictive capability, uncertainty measurement and interpretable structure. However, the original GPR has a heavy complexity, which limits its effectiveness on big data problems. Though plenty of sparse GPR methods were proposed to deal with it, they usually result in reducing the prediction accuracy. In this paper, a novel sparse GPR with a new defined objective function is proposed to obtain the hyperparameters in a different manner compared to traditional maximizing-likelihood. Experimental results on a real diesel engine data set and several public data sets verify that the proposed method can have a better performance on the prediction." @default.
- W4313203592 created "2023-01-06" @default.
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- W4313203592 date "2023-11-01" @default.
- W4313203592 modified "2023-10-09" @default.
- W4313203592 title "Nitrogen Oxides Concentration Estimation of Diesel Engines Based on a Sparse Nonstationary Trigonometric Gaussian Process Regression With Maximizing the Composite Likelihood" @default.
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- W4313203592 doi "https://doi.org/10.1109/tie.2022.3231246" @default.
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