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Rough Sets - International Joint Conference, IJCRS 2022, Suzhou, China, November 11-14, 2022, Proceedings (Paperback, 1st ed. 2022)
JingTao Yao, Hamido Fujita, Xiaodong Yue, Duoqian Miao, Jerzy Grzymala-Busse, …
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R2,499
Discovery Miles 24 990
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Ships in 10 - 15 working days
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This book constitutes the proceedings of the International Joint
Conference on Rough Sets, IJCRS 2022, held in Suzhou, China, in
November 2022. The 28 full papers included in this book were
carefully reviewed and selected from 42 submissions. They were
organized in topical sections as follows: Invited papers, IRSS
President Forum; rough set theory and applications; granular
computing and applications; classification and deep learning;
conceptual knowledge discovery and machine learning based on
three-way decisions and granular computing; uncertainty in
three-way decisions; granular computing, and data science.
Machine learning is widely used for data analysis. Dynamic fuzzy
data are one of the most difficult types of data to analyse in the
field of big data, cloud computing, the Internet of Things, and
quantum information. At present, the processing of this kind of
data is not very mature. The authors carried out more than 20 years
of research, and show in this book their most important results.
The seven chapters of the book are devoted to key topics such as
dynamic fuzzy machine learning models, dynamic fuzzy self-learning
subspace algorithms, fuzzy decision tree learning, dynamic concepts
based on dynamic fuzzy sets, semi-supervised multi-task learning
based on dynamic fuzzy data, dynamic fuzzy hierarchy learning,
examination of multi-agent learning model based on dynamic fuzzy
logic. This book can be used as a reference book for senior college
students and graduate students as well as college teachers and
scientific and technical personnel involved in computer science,
artificial intelligence, machine learning, automation, data
analysis, mathematics, management, cognitive science, and finance.
It can be also used as the basis for teaching the principles of
dynamic fuzzy learning.
This book explains deep learning concepts and derives
semi-supervised learning and nuclear learning frameworks based on
cognition mechanism and Lie group theory. Lie group machine
learning is a theoretical basis for brain intelligence,
Neuromorphic learning (NL), advanced machine learning, and advanced
artifi cial intelligence. The book further discusses algorithms and
applications in tensor learning, spectrum estimation learning,
Finsler geometry learning, Homology boundary learning, and
prototype theory. With abundant case studies, this book can be used
as a reference book for senior college students and graduate
students as well as college teachers and scientific and technical
personnel involved in computer science, artifi cial intelligence,
machine learning, automation, mathematics, management science,
cognitive science, financial management, and data analysis. In
addition, this text can be used as the basis for teaching the
principles of machine learning. Li Fanzhang is professor at the
Soochow University, China. He is director of network security
engineering laboratory in Jiangsu Province and is also the director
of the Soochow Institute of industrial large data. He published
more than 200 papers, 7 academic monographs, and 4 textbooks. Zhang
Li is professor at the School of Computer Science and Technology of
the Soochow University. She published more than 100 papers in
journals and conferences, and holds 23 patents. Zhang Zhao is
currently an associate professor at the School of Computer Science
and Technology of the Soochow University. He has authored and
co-authored more than 60 technical papers.
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