03458nam 2200505 450 991064778790332120230511003833.03-031-22371-310.1007/978-3-031-22371-6(MiAaPQ)EBC7192214(Au-PeEL)EBL7192214(CKB)26094846200041(DE-He213)978-3-031-22371-6(PPN)268205868(EXLCZ)992609484620004120230511d2023 uy 0engurcnu||||||||txtrdacontentcrdamediacrrdacarrierFusion of machine learning paradigms theory and applications /edited by Ioannis K. Hatzilygeroudis, George A. Tsihrintzis, Lakhmi C. Jain1st ed. 2023.Cham, Switzerland :Springer,[2023]©20231 online resource (204 pages)Intelligent Systems Reference Library,1868-4408 ;236Print version: Hatzilygeroudis, Ioannis K. Fusion of Machine Learning Paradigms Cham : Springer International Publishing AG,c2023 9783031223709 Includes bibliographical references.Editorial Note -- Artificial Intelligence as Dual-Use Technology -- Diabetic Retinopathy Detection using Transfer and Reinforcement Learning with effective image preprocessing and data augmentation techniques. .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. This book is directed toward professors, researchers, scientists, engineers and students in Machine Learning-related disciplines, as the hybridism presented, and the case studies described provide researchers with successful approaches and initiatives to efficiently address complex classification or regression problems. It is also directed toward readers who come from other disciplines, including Engineering, Medicine or Education Sciences, and are interested in becoming versed in some of the most recent Machine Learning-based technologies. Extensive lists of bibliographic references at the end of each chapter guide the readers to probe further into the application areas of interest to them.Intelligent Systems Reference Library,1868-4408 ;236Machine learningMachine learning.780Hatzilygeroudis IoannisTsihrintzis George A.Jain L. C.MiAaPQMiAaPQMiAaPQBOOK9910647787903321Fusion of machine learning paradigms3362952UNINA