Batch-Mode Active Learning of Gaussian Process Regression With Maximum Model Change-Reference-Cited by-同舟云学术

Batch-Mode Active Learning of Gaussian Process Regression With Maximum Model Change

Published:2023-12 Issue:12 Volume:53 Page:7894-7900
ISSN:2168-2216
Container-title:IEEE Transactions on Systems, Man, and Cybernetics: Systems
language:
Short-container-title:IEEE Trans. Syst. Man Cybern, Syst.

Author:

Zhao Yongyao¹^ORCID,Lin Jinxing¹^ORCID,Lin Jinping²,Wu Edmond Q.³^ORCID

Affiliation:

1. College of Automation and College of Artificial Intelligence, Nanjing University of Posts and Telecommunications, Nanjing, China

2. State Key Laboratory of Bioreactor Engineering, New World Institute of Biotechnology, East China University of Science and Technology, Shanghai, China

3. School of Electronic Information and Electrical Engineering, Shanghai Jiao Tong University, Shanghai, China

Funder

National Key Research and Development Program of China

National Natural Science Foundation for Distinguished Young Scholars of China

National Natural Science Foundation of China

Shanghai Municipal Science and Technology Major Project

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Electrical and Electronic Engineering,Computer Science Applications,Human-Computer Interaction,Control and Systems Engineering,Software

Link

http://xplorestaging.ieee.org/ielx7/6221021/10321818/10296951.pdf?arnumber=10296951

Reference21 articles.

1. Active Learning for Gaussian Process Considering Uncertainties With Application to Shape Control of Composite Fuselage

2. Neural Network Exploration Using Optimal Experiment Design

3. Pool-Based Sequential Active Learning for Regression

4. Safe Exploration for Active Learning with Gaussian Processes

5. Accurate quantitative estimation of energy performance of residential buildings using statistical machine learning tools