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Home > Computing and Information Technology > Computer science > Artificial intelligence > Machine learning > Hybrid Deep Learning Networks Based on Self-Organization and their Applications
Hybrid Deep Learning Networks Based on Self-Organization and their Applications

Hybrid Deep Learning Networks Based on Self-Organization and their Applications


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About the Book

This monograph is devoted to the consideration of new deep neural networks – Evolving Hybrid Stacking Neuro-Neo-Fuzzy Systems of Artificial Intelligence based on Group Method of Data Handling, which, in turn, is the first known method of deep learning. This method is based on the principle of self-organization and, unlike in other deep learning methods, it allows not only to adjust the weights of neural connections, but also to synthesize the optimal network structure in online mode. The proposed approach allows you to process information in online mode and solve a wide class of Data Stream Mining problems. Particular attention in the book is paid to the online bagging approach, when optimal accuracy results are synthesized for solving the problems of pattern recognition, forecasting, and classification. The book is aimed primarily at specialists in the field of deep learning involved in the development of new architectures and algorithms for deep learning networks and their application in forecasting, pattern recognition and medical diagnostics, but will also be of use to students of computer science and AI, and the general scientific community at large.

About the Author :
Bodyanskiy Yevgeniy graduated with honours from Kharkiv National University of Radio Electronics (KhNURE) in 1971. In 1980 he defended his doctoral thesis. In 1990 he was awarded a Dr.habil.sc.ing. degree. In 1994, he was awarded the academic title of Professor. His major fields of research are evolving hybrid systems of computational intelligence, data stream mining, and big data. Zaychenko Yuriy graduated from Kiev Polytechnic Institute (KPI) in 1965. In 1968 he was conferred a PhD degree. In 1983 he became a full professor at KPI. From 1997 to the present, he has been working as a professor in the Institute for Applied System Analysis, KPI. His scientific interests include decision-making under uncertainty and risk, artificial intelligence, computer networks modelling and optimization He was awarded the Ukrainian state prize in the field of science and technology in 2011. Galib Hamidov received his PhD degree at the Baku Institute of Information Technology in 2012. From 2006 to the present, he has been working as a director in the Information Technology Department of "Azerishig" Electrical Distribution Company. His scientific interests include neural networks, deep learning, and hybrid neo-fuzzy neural networks.


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Product Details
  • ISBN-13: 9781036414313
  • Publisher: Cambridge Scholars Publishing
  • Publisher Imprint: Cambridge Scholars Publishing
  • Edition: Unabridged edition
  • Language: English
  • Width: 148 mm
  • ISBN-10: 1036414310
  • Publisher Date: 30 Oct 2024
  • Binding: Hardback
  • Height: 212 mm
  • No of Pages: 133


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