Multi-Task Learning Based on Stochastic Configuration Networks
- Collaborative Innovation Center of Statistical Data Engineering, Technology and Application, School of Statistics and Mathematics, Zhejiang Gongshang University, Hangzhou, China
A Corrigendum on
Multi-task learning based on Stochastic Configuration Networks
by Dong X-M, Kong X and Zhang X (2022). Front. Bioeng. Biotechnol. 10:890132. doi: 10.3389/fbioe.2022.890132
In the published article, there was an error in the Article Title. Instead of “Multi-Task Learning Based on Stochastic Configuration Neural Networks”, it should be “Multi-Task Learning Based on Stochastic Configuration Networks.”
In the published article, there was an error. The authors of “Stochastic Configuration Networks” said that “Stochastic Configuration Networks” is a proper noun. It cannot be written as “Stochastic Configuration Neural Networks.”
A correction has been made to Abstract, paragraph 1. This sentence previously stated:
“This paper proposes a multi-task learning framework based on stochastic configuration neural networks. It organically combines the idea of the classical parameter sharing multi-task learning with that of constraint sharing configuration in stochastic configuration neural networks.”
The corrected sentence appears below:
“This paper proposes a multi-task learning framework based on stochastic configuration networks. It organically combines the idea of the classical parameter sharing multi-task learning with that of constraint sharing configuration in stochastic configuration networks.”
The authors apologize for these error and state that this does not change the scientific conclusions of the article in any way. The original article has been updated.
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Keywords: multi-task learning, neural networks, stochastic configuration, knowledge sharing and transfer, supervised mechanism
Citation: Dong X-M, Kong X and Zhang X (2023) Corrigendum: Multi-task learning based on Stochastic Configuration Networks. Front. Bioeng. Biotechnol. 11:1224382. doi: 10.3389/fbioe.2023.1224382
Received: 17 May 2023; Accepted: 31 July 2023;
Published: 01 September 2023.
Edited and reviewed by:
Gongfa Li, Wuhan University of Science and Technology, ChinaCopyright © 2023 Dong, Kong and Zhang. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
*Correspondence: Xudong Kong, simon_kong@163.com