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Machine Learning under Resource Constraints
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  • Title: Machine Learning under Resource Constraints
  • Author(s) Katharina Morik, Jörg Rahnenführer, Christian Wietfeld, Peter Marwedel, Wolfgang Rhode
  • Publisher: De Gruyter (2023); eBook (Creative Commons Licensed)
  • License(s): Creative Commons License (CC)
  • Paperback: N/A
  • eBook: PDF and ePub
  • Language: English
  • ISBN-10/ASIN: N/A
  • ISBN-13: 978-3111135816
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Book Description

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. Comprehensive overview of novel approaches to machine learning research that consider resource constraints and application of the described methods.

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.

About the Authors
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