Machine Learning Under Resource Constraints - Applications
Machine Learning under Resource Constraints addresses novel machine learning algorithms that are challenged by high-throughput data, by high dimensions, or by complex structures of the data in three volumes. Resource constraints are given by the relation between the demands for processing the data a...
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| Formato: | Libro |
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De Gruyter
2023
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=nlebk&AN=3501568&site=ehost-live header: @attributes: shortDbName: nlebk uiTerm: 3501568 longDbName: eBook Collection (EBSCOhost) uiTag: AN controlInfo: bkinfo: btl: Machine Learning Under Resource Constraints - Applications aug: au: Katharina Morik Jörg Rahnenführer Christian Wietfeld sertl: Machine Learning Under Resource Constraints isbn: 9783110785975 9783110785982 9783110786149 imageinfo: pubinfo: dt: @attributes: year: 2023 month: 01 day: 01 dtAvail: @attributes: year: 2023 month: 02 day: 10 vid: 00003 pub: De Gruyter pubContract: De Gruyter place: Berlin price: 0.01 limitsGroup: maxCheckoutDays: 1500 pda: N printPagesOffline: 100 printPagesOnline: 100 previewPages: 10000 prePubGroup: dewey: @attributes: class: 006.31 item: 006 .31 lc: @attributes: class: Q325.5 item: Q 325 .5 artinfo: ui: 3501568 1356978758 formats: fmt: – @attributes: type: EB doid: NL$3501568$PDF caption: PDF download: Y – @attributes: type: EK doid: NL$3501568$EPUB caption: EPUB download: Y tig: atl: Machine Learning Under Resource Constraints - Applications ptl: Machine Learning Under Resource Constraints - Applications aug: au: Katharina Morik Jörg Rahnenführer Christian Wietfeld su: Physics--Data processing Machine learning sug: subj: COMPUTERS / Information Technology COMPUTERS / Data Science / General COMPUTERS / Data Science / Data Analytics COMPUTERS / Programming / Algorithms SCIENCE / Chemistry / General Physics--Data processing Machine learning ab: Machine Learning under Resource Constraints addresses novel machine learning algorithms that are challenged by high-throughput data, by high dimensions, or by complex structures of the data in three volumes. Resource constraints are given by the relation between the demands for processing the data and the capacity of the computing machinery. The resources are runtime, memory, communication, and energy. Hence, modern computer architectures play a significant role. Novel machine learning algorithms are optimized with regard to minimal resource consumption. Moreover, learned predictions are executed on diverse architectures to save resources. It provides a comprehensive overview of the novel approaches to machine learning research that consider resource constraints, as well as the application of the described methods in various domains of science and engineering. Volume 3 describes how the resource-aware machine learning methods and techniques are used to successfully solve real-world problems. The book provides numerous specific application examples. In the areas of health and medicine, it is demonstrated how machine learning can improve risk modelling, diagnosis, and treatment selection for diseases. Machine learning supported quality control during the manufacturing process in a factory allows to reduce material and energy cost and save testing times is shown by the diverse real-time applications in electronics and steel production as well as milling. Additional application examples show, how machine-learning can make traffic, logistics and smart cities more effi cient and sustainable. Finally, mobile communications can benefi t substantially from machine learning, for example by uncovering hidden characteristics of the wireless channel. pubtype: eBook doctype: Book ougenre: Book language: English copyright: @attributes: flag: N copyrightText: holdings: @attributes: islocal: N |
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