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Machine Learning under Resource Constraints. Applications / / ed. by Katharina Morik, Christian Wietfeld, Jörg Rahnenführer



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Titolo: Machine Learning under Resource Constraints. Applications / / ed. by Katharina Morik, Christian Wietfeld, Jörg Rahnenführer Visualizza cluster
Pubblicazione: Berlin ; ; Boston : , : De Gruyter, , [2022]
©2023
Edizione: 1st ed.
Descrizione fisica: 1 online resource (VIII, 470 p.)
Disciplina: 006.31
Soggetto topico: SCIENCE / Chemistry / General
Soggetto non controllato: Artificial Intelligence
Big Data and Machine Learning
Cyber-physical systems
Data mining for Ubiquitous System Software
Embedded Systems and Machine Learning
Highly Distributed Data
ML on Small devices
Machine learning for knowledge discovery
Machine learning in high-energy physics
Resource-Aware Machine Learning
Resource-Constrained Data Analysis
Persona (resp. second.): ArendtChristian
AwasthiShrutarv
BektasCaner
BiskupJoachim
BorgwardtKarsten
BöckerStefan
BüscherJan
ChengLiang
DeuseJochen
DingZeyu
FalkenbergRobert
FinkeldeyFelix
GramseNils
HaferkampMarcus
HeimannKarsten
HergenröderRoland
HompelMichael ten
HägerSimon
IckstadtKatja
JutzelerCatherine
JörkePascal
KriegeNils
KriegerCedrik
KurtzFabian
KösterJohannes
LangMichel
LiebigThomas
MachadoMaximillian
MasoudinejadMojtaba
MorikKatharina
MunteanuAlexander
MutzelPetra
OverbeckDennis
PanuschThorben
RahmannSven
RahnenführerJörg
RanRan
ReiningChristopher
RichterJakob
RoidlMoritz
SaadallahAmal
SchrammAlexander
SchreckenbergMichael
SchäferTill
ShpacovitchVictoria
SliwaBenjamin
StolpeMarco
StöckerBianca K
TiemannJanis
VrankenTim
WeichertFrank
WiederkehrPetra
WietfeldChristian
WüstefeldKonstantin
Nota di contenuto: 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
Sommario/riassunto: 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.
Titolo autorizzato: Machine Learning under Resource Constraints  Visualizza cluster
ISBN: 3-11-078598-6
Formato: Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione: Inglese
Record Nr.: 9910774815903321
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Serie: De Gruyter STEM Series