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Titel:Machine Learning under Resource Constraints
Titelzusatz:Applications
Mitwirkende:Morik, Katharina [HerausgeberIn]   i
 Rahnenführer, Jörg [HerausgeberIn]   i
 Wietfeld, Christian [HerausgeberIn]   i
 Arendt, Christian [MitwirkendeR]   i
 Awasthi, Shrutarv [MitwirkendeR]   i
 Bektas, Caner [MitwirkendeR]   i
 Biskup, Joachim [MitwirkendeR]   i
 Borgwardt, Karsten [MitwirkendeR]   i
 Böcker, Stefan [MitwirkendeR]   i
 Büscher, Jan [MitwirkendeR]   i
 Cheng, Liang [MitwirkendeR]   i
 Deuse, Jochen [MitwirkendeR]   i
 Ding, Zeyu [MitwirkendeR]   i
 Falkenberg, Robert [MitwirkendeR]   i
 Finkeldey, Felix [MitwirkendeR]   i
 Gramse, Nils [MitwirkendeR]   i
 Haferkamp, Marcus [MitwirkendeR]   i
 Heimann, Karsten [MitwirkendeR]   i
 Hergenröder, Roland [MitwirkendeR]   i
 Hompel, Michael ten [MitwirkendeR]   i
 Häger, Simon [MitwirkendeR]   i
 Ickstadt, Katja [MitwirkendeR]   i
 Jutzeler, Catherine [MitwirkendeR]   i
 Jörke, Pascal [MitwirkendeR]   i
 Kriege, Nils [MitwirkendeR]   i
 Krieger, Cedrik [MitwirkendeR]   i
 Kurtz, Fabian [MitwirkendeR]   i
 Köster, Johannes [MitwirkendeR]   i
 Lang, Michel [MitwirkendeR]   i
 Liebig, Thomas [MitwirkendeR]   i
 Machado, Maximillian [MitwirkendeR]   i
 Masoudinejad, Mojtaba [MitwirkendeR]   i
 Munteanu, Alexander [MitwirkendeR]   i
 Mutzel, Petra [MitwirkendeR]   i
 Overbeck, Dennis [MitwirkendeR]   i
 Panusch, Thorben [MitwirkendeR]   i
 Rahmann, Sven [MitwirkendeR]   i
 Ran, Ran [MitwirkendeR]   i
 Reining, Christopher [MitwirkendeR]   i
 Richter, Jakob [MitwirkendeR]   i
 Roidl, Moritz [MitwirkendeR]   i
 Saadallah, Amal [MitwirkendeR]   i
 Schramm, Alexander [MitwirkendeR]   i
 Schreckenberg, Michael [MitwirkendeR]   i
 Schäfer, Till [MitwirkendeR]   i
 Shpacovitch, Victoria [MitwirkendeR]   i
 Sliwa, Benjamin [MitwirkendeR]   i
 Stolpe, Marco [MitwirkendeR]   i
 Stöcker, Bianca K [MitwirkendeR]   i
 Tiemann, Janis [MitwirkendeR]   i
 Vranken, Tim [MitwirkendeR]   i
 Weichert, Frank [MitwirkendeR]   i
 Wiederkehr, Petra [MitwirkendeR]   i
 Wüstefeld, Konstantin [MitwirkendeR]   i
Verf.angabe:edited by Katharina Morik, Christian Wietfeld, Jörg Rahnenführer
Verlagsort:Berlin ; Boston
Verlag:De Gruyter
Jahr:2022
Umfang:1 Online-Ressource (VIII, 470 Seiten)
Gesamttitel/Reihe:Machine Learning under Resource Constraints ; Volume 3
 De Gruyter STEM
Schrift/Sprache:In English
Ang. zum Inhalt:Frontmatter
 Contents
 1 Editorial
 2 Health / Medicine
 2.1 Machine Learning in Medicine
 2.2 Virus Detection
 2.3 Cancer Diagnostics and Therapy from Molecular Data
 2.4 Bayesian Analysis for Dimensionality and Complexity Reduction
 2.5 Survival Prediction and Model Selection
 2.6 Protein Complex Similarity
 3 Industry 4.0
 3.1 Keynote on Industry 4.0
 3.2 Quality Assurance in Interlinked Manufacturing Processes
 3.3 Label Proportion Learning
 3.4 Simulation and Machine Learning
 3.5 High-Precision Wireless Localization
 3.6 Indoor Photovoltaic Energy Harvesting
 3.7 Micro-UAV Swarm Testbed for Indoor Applications
 4 Smart City and Traffic
 4.1 Inner-City Traffic Flow Prediction with Sparse Sensors
 4.2 Privacy-Preserving Detection of Persons and Classification of Vehicle Flows
 4.3 Green Networking and Resource Constrained Clients for Smart Cities
 4.4 Vehicle to Vehicle Communications: Machine Learning-Enabled Predictive Routing
 4.5 Modelling of Hybrid Vehicular Traffic with Extended Cellular Automata
 4.6 Embedded Crowdsensing for Pavement Monitoring and its Incentive Mechanisms
 5 Communication Networks
 5.1 Capacity Analysis of IoT Networks in the Unlicensed Spectrum
 5.2 Resource-Efficient Vehicle-to-Cloud Communications
 5.3 Mobile-Data Network Analytics Highly Reliable Networks
 5.4 Machine Learning-Enabled 5G Network Slicing
 5.5 Potential of Millimeter Wave Communications
 6 Privacy
 6.1 Keynote: Construction of Inference-Proof Agent Interactions
 Bibliography
 Index
 List of Contributors
ISBN:978-3-11-078598-2
 978-3-11-078614-9
Abstract: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
DOI:doi:10.1515/9783110785982
URL:kostenfrei: Resolving-System: https://doi.org/10.1515/9783110785982
 kostenfrei: Verlag: https://www.degruyter.com/isbn/9783110785982
 Cover: https://www.degruyter.com/document/cover/isbn/9783110785982/original
 DOI: https://doi.org/10.1515/9783110785982
Schlagwörter:(s)Künstliche Intelligenz   i / (s)Maschinelles Lernen   i / (s)Eingebettetes System   i / (s)Big Data   i
Datenträger:Online-Ressource
Sprache:eng
(Sekundärform):Issued also in print
Bibliogr. Hinweis:Erscheint auch als : Druck-Ausgabe: Machine learning under resource constraints ; Volume 3: Applications. - Berlin : De Gruyter, 2023. - VIII, 470 Seiten
RVK-Notation:ST 300   i
Sach-SW:Computer aided manufacture (CAM)
 Computeranwendungen in Industrie und Technologie
 Data Mining
 Data mining
 Information technology: general issues
 Machine learning
 Maschinelles Lernen
 Praktische Anwendung für Informationstechnologien
 SCIENCE / Chemistry / General
K10plus-PPN:1831669501
 
 
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