Gaussian process regression with heteroscedastic noises — A machine-learning predictive variance approach-Reference-Cited by-同舟云学术

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Gaussian process regression with heteroscedastic noises — A machine-learning predictive variance approach

Published:2020-05 Issue: Volume:157 Page:162-173
ISSN:0263-8762
Container-title:Chemical Engineering Research and Design
language:en
Short-container-title:Chemical Engineering Research and Design

Author:

Li Zhenxing,Hong Xiaodan,Hao Kuangrong,Chen Lei,Huang Biao^ORCID

Funder

Ministry of Science and Technology

National Natural Science Foundation of China

Natural Science Foundation of Shanghai

Fundamental Research Funds for the Central Universities

Donghua University

Natural Science and Engineering Research Council of Canada

Publisher

Elsevier BV

Subject

General Chemical Engineering,General Chemistry

Reference42 articles.

1. Robust insulin estimation under glycemic variability using Bayesian filtering and Gaussian process models;Avila;Biomed. Signal Process. Control,2018

2. Bagging for Gaussian process regression;Chen;Neurocomputing,2009

3. Review and performance comparison of SVM-and ELM-based classifiers;Chorowski;Neurocomputing,2014

4. The nature of noise;Edmonds;Lect. Notes Comput. Sci.,2009

5. Prognosis of bearing acoustic emission signals using supervised machine learning;Elforjani;IEEE Trans. Ind. Electron.,2018

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