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Titlebook: Fusion of Machine Learning Paradigms; Theory and Applicati Ioannis K. Hatzilygeroudis,George A. Tsihrintzis,L Book 2023 The Editor(s) (if a

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書目名稱Fusion of Machine Learning Paradigms
副標(biāo)題Theory and Applicati
編輯Ioannis K. Hatzilygeroudis,George A. Tsihrintzis,L
視頻videohttp://file.papertrans.cn/351/350959/350959.mp4
概述Provides a concise coverage of the fusion of Machine Learning Paradigms from the vantage point of a newcomer.Presents the most recent advances in fusing deep learning with other learning paradigms.Con
叢書名稱Intelligent Systems Reference Library
圖書封面Titlebook: Fusion of Machine Learning Paradigms; Theory and Applicati Ioannis K. Hatzilygeroudis,George A. Tsihrintzis,L Book 2023 The Editor(s) (if a
描述.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 i
出版日期Book 2023
關(guān)鍵詞Intelligent Systems; Artificial Intelligence; Applied AI; Machine Learning; Deep Learning; Deep Neural Ne
版次1
doihttps://doi.org/10.1007/978-3-031-22371-6
isbn_softcover978-3-031-22373-0
isbn_ebook978-3-031-22371-6Series ISSN 1868-4394 Series E-ISSN 1868-4408
issn_series 1868-4394
copyrightThe Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerl
The information of publication is updating

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