Fundamentals
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Contributor(s)
Morik, Katharina (editor)
Marwedel, Peter (editor)
Language
EnglishAbstract
Volume 1 establishes the foundations of this new field. It goes through all the steps from data collection, their summary and clustering, to different aspects of resource-aware learning, i.e., hardware, memory, energy, and communication awareness. Machine learning methods are inspected with respect to resource requirements and how to enhance scalability on diverse computing architectures ranging from embedded systems to large computing clusters.
Keywords
Resource-Constrained Data Analysis; Resource-Aware Machine Learning; Embedded Systems and Machine Learning; Big Data and Machine Learning; Artificial Intelligence; Highly Distributed Data; ML on Small devices; Data mining for Ubiquitous System Software Cyber-physical systems Machine learning in high-energy physics Machine learning for knowledge discovery; thema EDItEUR::P Mathematics and Science::PN Chemistry; thema EDItEUR::U Computing and Information Technology::UM Computer programming / software engineering::UMB Algorithms and data structures; thema EDItEUR::U Computing and Information Technology::UN Databases; thema EDItEUR::U Computing and Information Technology::UT Computer networking and communications; thema EDItEUR::U Computing and Information Technology::UY Computer science::UYQ Artificial intelligenceISBN
9783110785944, 9783110785937, 9783110786125Publisher
De GruyterPublisher website
http://www.degruyter.com/Publication date and place
Berlin/Boston, 2022Imprint
De GruyterSeries
De Gruyter STEM,Classification
Chemistry
Algorithms and data structures
Databases
Computer networking and communications
Artificial intelligence
Pages
491
Except where otherwise noted, this item's license is described
as
open access