Machine Learning Under Resource Constraints - Applications
Language: English
Published by De Gruyter, 2022
- Softcover
- New
Seller: Revaluation Books, Exeter, United KingdomRevaluation Books
AbeBooks seller since January 6, 2003
Condition: New
£ 109.41
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Add to basketItem description from seller
478 pages. 9.45x6.69x1.06 inches. In Stock.
Seller Inventory # __3110785978
- Title
- Machine Learning Under Resource Constraints - Applications
- Author
- Katharina Morik/ Christian Wietfeld/ Jörg Rahnenführer/ Jens Buß/ Andreas Becker
- Publisher
- De Gruyter
- Publication year
- 2022
- Condition
- Brand New
- Binding
- Paperback
- Language
- English
- ISBN 10
- 3110785978
- ISBN 13
- 9783110785975
- Item weight
- 0.79 kilograms
- Series
- Book 171 of 256: De Gruyter STEM
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.
"Synopsis" may belong to another edition of this title.
About the Author
Jörg Rahnenführer: Prof. Dr. Jörg Rahnenführer is professor for "Statistical methods in genetics and chemometrics" at the Department of Statistics at TU Dortmund University. After obtaining a PhD in mathematics from the University of Düsseldorf he worked as a postdoc in Vienna, Berkeley, Omaha, and at the Max Planck Institute for Informatics in Saarbrücken. The respective departments covered a wide range of fields, including mathematics, statistics, biostatistics, genetics, and computer science. His group at TU Dortmund develops and applies in interdisciplinary projects statistical methods mainly for applications in bioinformatics, toxicology, and medicine. He has particularly worked successfully on the meaningful exploitation of high-dimensional omics data and as a member of the SFB 876 on hyperparameter optimization in statistical learning methods. Since 2021 he is the spokesperson of the DFG-funded Research Training Group (RTG) 2624 "Biostatistical Methods for High-Dimensional Data in Toxicology".
"About the title" may belong to another edition of this title.
Revaluation Books
Exeter, United Kingdom
AbeBooks seller since January 6, 2003
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