Synopsis
This book is the culmination of our research in the recent decade on randomized neural networks with data-dependent supervision mechanisms. Traditional randomized neural networks mainly focused on constructing various deep neural networks with data independent random weights, ignoring the impact of the number of nodes and scope of parameters on the universal approximation property (UAP) of randomized neural networks. Comprising of 15 chapters, Advanced Randomized Neural Networks for Pattern Analysis introduces systematic solutions for advanced data-dependent stochastic configuration networks, namely algorithms that assign random parameters and construct network structures incrementally. The book is segmented into three major sections — neural networks optimization, robust data analysis, and deep fusion learning — that feature the successful performance of advanced randomized neural networks in various pattern analysis problems. We anticipate that both researchers and engineers in the field of artificial neural networks, particularly pattern recognition and medical diagnosis, will find this book and the associated algorithms useful, and we hope that anyone with an interest in the related research field will find the book enjoyable and informative.
About the Authors
Chenglong Zhang (Member, IEEE) received his B.Sc. degree from the Qufu Normal University, Rizhao, China, in 2014, his M.Sc. degree from the Guizhou University, Guiyang, China, in 2017, and his PhD degree from the School of Computer Science and Technology, China University of Mining and Technology, Xuzhou, China, in 2023. He is currently a Postdoctoral Fellow with The Chinese University of Hong Kong, Shenzhen and University of Science and Technology of China. His main research interests include randomized neural networks, deep stochastic configuration networks, multi-modal data fusion, medical biometrics. He serves as Young Editorial Board Member of Journal of Artificial Intelligence & Control Systems (JAICS) and Journal of Shandong University of Science and Technology (Natural Science) . He has published more than 30 papers in international conferences and journals.
Shifei Ding (Fellow, CAAI; Senior Member, IEEE) received his PhD degree from Shandong University of Science and Technology, Taian, China in 2004. He received Postdoctoral Fellow from Key Laboratory of Intelligent Information Processing (IIP), Institute of Computing Technology (ICT), and the Chinese Academy of Sciences (CAS). He is a professor and PhD supervisor at the China University of Mining and Technology. His research interests include intelligent information processing, pattern recognition, machine learning, data mining, and granular computing. He has published 6 books, and more than around 200 papers in international conferences and journals. Prof. Ding has been selected as a Fellow of Chinese Association for Artificial Intelligence (CAAI) in 2024.
Yang Wang received the B.Sc. degree from the Xuchang University, Xuchang, China, in 2011, his M.Sc. degree from the Guizhou University, Guiyang, China, in 2014, and his PhD degree from the Chinese Academy of Sciences, Chengdu, China, in 2018. He is currently a Lecturer at the State Key Laboratory of Public Big Data, Guizhou University. His main research interests include stochastic configuration networks, industrial artificial intelligence, deep learning.
David Zhang (Corresponding Author) (Life Fellow, IEEE) graduated from Peking University, Beijing, China, in 1974 and received his MS and first PhD degrees in computer science from the Harbin Institute of Technology, Harbin, China, in 1982 and 1985, respectively. He also got his second PhD degree in electrical and computer engineering from the University of Waterloo, ON, Canada, in 1994. From 1986 to 1988, he was a Postdoctoral Fellow at Tsinghua University, Beijing, and then an Associate Professor at the Institute of Automation, Chinese Academy of Sciences, Beijing. He has been a Chair Professor at the Hong Kong Polytechnic University, Hong Kong, where he is the Founding Director of the Biometrics Research Centre (UGC/CRC) supported by the Hong Kong SAR Government since 1998. He is currently a Distinguished Presidential Chair Professor at the Chinese University of Hong Kong, Shenzhen, China. Over the past 40 years, he has been working on pattern recognition, image processing, and biometrics, where many research results have been awarded and some created directions, including medical biometrics and computerized TCM, all of which are famous in the world. He has published 20+ monographs, 500+ international journal papers, and 50+ patents from the USA, Japan and China. For eight years, he has been continuously listed as a Global Highly Cited Researcher in Engineering by Clarivate Analytics. He is also ranked 73th with H-Index 130 at Top 1,000 Scientists for International Computer Science in 2024. Prof. Zhang has been selected as a Fellow of both Royal Society of Canada (RSC) and Canadian Academy of Engineering (CAE). He is also a Croucher Senior Research Fellow, a Distinguished Speaker of the IEEE Computer Society, and an IAPR and AAIA Fellow.
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