Adaptive Resonance Theory-Based Topological Clustering With a Divisive Hierarchical Structure Capable of Continual Learning-Reference-Cited by-同舟云学术

Adaptive Resonance Theory-Based Topological Clustering With a Divisive Hierarchical Structure Capable of Continual Learning

Published:2022 Issue: Volume:10 Page:68042-68056
ISSN:2169-3536
Container-title:IEEE Access
language:
Short-container-title:IEEE Access

Author:

Masuyama Naoki¹^ORCID,Amako Narito²,Yamada Yuna²,Nojima Yusuke¹^ORCID,Ishibuchi Hisao³^ORCID

Affiliation:

1. Graduate School of Informatics, Osaka Metropolitan University, Sakai-shi, Osaka, Japan

2. Graduate School of Engineering, Osaka Prefecture University, Sakai-shi, Osaka, Japan

3. Department of Computer Science and Engineering, Guangdong Provincial Key Laboratory of Brain-Inspired Intelligent Computation, Southern University of Science and Technology, Shenzhen, China

Funder

Ministry of Education, Culture, Sports, Science and Technology—Japan (MEXT) Leading Initiative for Excellent Young Researchers

National Natural Science Foundation of China

Guangdong Provincial Key Laboratory

Program for Guangdong Introducing Innovative and Enterpreneurial Teams

Stable Support Plan Program of Shenzhen Natural Science Fund

Shenzhen Science and Technology Program

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

General Engineering,General Materials Science,General Computer Science,Electrical and Electronic Engineering

Link

http://xplorestaging.ieee.org/ielx7/6287639/9668973/09807317.pdf?arnumber=9807317

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5. Learning under Concept Drift: A Review

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