TY  - GEN
AB  - This book constitutes the refereed proceedings of the 23rd International Conference on Intelligent Data Engineering and Automated Learning, IDEAL 2022, which took place in Manchester, UK, during November 24-26, 2022. The 52 full papers included in this book were carefully reviewed and selected from 79 submissions. They deal with emerging and challenging topics in intelligent data analytics and associated machine learning paradigms and systems. Special sessions were held on clustering for interpretable machine learning; machine learning towards smarter multimodal systems; and computational intelligence for computer vision and image processing.
AU  - Yin, Hujun,
AU  - Camacho, David,
AU  - Tino, Peter,
CN  - QA76.9.D3
DO  - 10.1007/978-3-031-21753-1
DO  - doi
ID  - 1451590
KW  - Database management
KW  - Data mining
KW  - Intelligent agents (Computer software)
LK  - https://univsouthin.idm.oclc.org/login?url=https://link.springer.com/10.1007/978-3-031-21753-1
N1  - Conference proceedings.
N2  - This book constitutes the refereed proceedings of the 23rd International Conference on Intelligent Data Engineering and Automated Learning, IDEAL 2022, which took place in Manchester, UK, during November 24-26, 2022. The 52 full papers included in this book were carefully reviewed and selected from 79 submissions. They deal with emerging and challenging topics in intelligent data analytics and associated machine learning paradigms and systems. Special sessions were held on clustering for interpretable machine learning; machine learning towards smarter multimodal systems; and computational intelligence for computer vision and image processing.
SN  - 9783031217531
SN  - 3031217535
T1  - Intelligent data engineering and automated learning -- IDEAL 2022 :23rd International Conference, Manchester, UK, November 24-26, 2022 : proceedings /
TI  - Intelligent data engineering and automated learning -- IDEAL 2022 :23rd International Conference, Manchester, UK, November 24-26, 2022 : proceedings /
UR  - https://univsouthin.idm.oclc.org/login?url=https://link.springer.com/10.1007/978-3-031-21753-1
VL  - 13756
ER  -