Название: Fusion of Machine Learning Paradigms: Theory and Applications
Автор: Ioannis K. Hatzilygeroudis, George A. Tsihrintzis, Lakhmi C. Jain
Издательство: Springer
Год: 2023
Страниц: 204
Язык: английский
Формат: pdf (true), epub
Размер: 28.3 MB

This book aims at updating the relevant computer science-related research communities, including professors, researchers, scientists, engineers and students, as well as the general reader from other disciplines, on the most recent advances in applications of methods based on Fusing Machine Learning Paradigms. Integrated or Hybrid Machine Learning methodologies combine together two or more Machine Learning approaches achieving higher performance and better efficiency when compared to those of their constituent components and promising major impact in science, technology and the society. The book consists of an editorial note and an additional eight chapters and is organized into two parts, namely: (i) Recent Application Areas of Fusion of Machine Learning Paradigms and (ii) Applications that can clearly benefit from Fusion of Machine Learning Paradigms.

One of the most successful and most promising Machine Learning approaches that, in recent years, have claimed a large part of research activities is that of Integrated or Hybrid Approaches and Methodologies. These are approaches that combine or fuse together two or more component methodologies and include neuro-symbolic integrations, either rule-based or logic-based, or the combination of Machine Learning approaches with fuzzy logic, evolutionary algorithms, artificial immune systems, or other statistical methods. Various important applications indicate that fusing together various Machine Learning approaches results in methodologies that benefit from each of their constituent components and, thus, outperform them. The book at hand aims at updating the relevant Artificial Intelligence, Software Engineering, and Computer Science-related research communities, including professors, researchers, scientists, engineers, and students, on the most recent advances in applications of methods based on Fusing Machine Learning Paradigms.

Even though the editors undertook a difficult task, namely to present a representative account of Fusion of Machine Learning Paradigms, the coverage of the book theme is impressive in terms of both breadth and depth. On one hand, the presented case studies are quite diverse, and, on the other hand, each case study is presented in depth. Undoubtedly, readers with a background in Machine Learning, Artificial Intelligence, Software Engineering, and Computer Science will find the book particularly helpful in their researches. However, I am confident that interest will also be stirred among general readers who are seeking to be versed in current methodologies of Fusion of Machine Learning Paradigms. I, thus, congratulate the editors for their superb job, and I highly recommend this timely book to both Machine Learning/Artificial Intelligence/Software Engineering/Computer Science researchers and general readers.








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