Volume 1-3 [Set Machine Learning under Resource Constraints, 3 volumes]: Fundamentals, Discovery in Physics, Applications

Print Only Published by De Gruyter 2023

Volume 1-3 [Set Machine Learning under Resource Constraints, 3 volumes]

Fundamentals, Discovery in Physics, Applications

Edited by: Katharina Morik , Jörg Rahnenführer and Christian Wietfeld

Part of the multi-volume work Machine Learning under Resource Constraints

In the series De Gruyter STEM

Overview
Volumes in this set

About this book

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.

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Book Open Access 2023

Volume 1 Machine Learning under Resource Constraints - Fundamentals

Katharina Morik, Peter Marwedel

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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 1 establishes the foundations of this new field. It goes through all the steps from data collection, their summary and clustering, to the different aspects of resource-aware learning, i.e., hardware, memory, energy, and communication awareness. Several machine learning methods are inspected with respect to their resource requirements and how to enhance their scalability on diverse computing architectures ranging from embedded systems to large computing clusters.

DOI:: https://doi.org/10.1515/9783110785944
ISBN:: 9783110785944
ISBN:: 9783110786125
ISBN:: 9783110785937
Subject:: Computer Sciences
Subject:: Artificial Intelligence
Subject:: Information Technology
Subject:: Algorithms
Subject:: Databases and Data Mining
Publisher:: De Gruyter

Book Open Access 2023

Volume 2 Machine Learning under Resource Constraints - Discovery in Physics

Katharina Morik, Wolfgang Rhode

More Cite Download EPUB Download PDF

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 2 covers machine learning for knowledge discovery in particle and astroparticle physics. Their instruments, e.g., particle detectors or telescopes, gather petabytes of data. Here, machine learning is necessary not only to process the vast amounts of data and to detect the relevant examples efficiently, but also as part of the knowledge discovery process itself. The physical knowledge is encoded in simulations that are used to train the machine learning models. At the same time, the interpretation of the learned models serves to expand the physical knowledge. This results in a cycle of theory enhancement supported by machine learning.

DOI:: https://doi.org/10.1515/9783110785968
ISBN:: 9783110785968
ISBN:: 9783110786132
ISBN:: 9783110785951
Subject:: Computer Sciences
Subject:: Artificial Intelligence
Subject:: Information Technology
Subject:: Algorithms
Subject:: Databases and Data Mining
Publisher:: De Gruyter

Book Open Access 2023

Volume 3 Machine Learning under Resource Constraints - Applications

Katharina Morik, Jörg Rahnenführer, Christian Wietfeld

More Cite Download EPUB Download PDF

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:: https://doi.org/10.1515/9783110785982
ISBN:: 9783110785982
ISBN:: 9783110786149
ISBN:: 9783110785975
Subject:: Computer Sciences
Subject:: Artificial Intelligence
Subject:: Information Technology
Subject:: Algorithms
Subject:: Databases and Data Mining
Publisher:: De Gruyter

(Deutsch)

Language: English
Publisher: De Gruyter
Pages
- Main content: 1346
Keywords: Künstliche Intelligenz; Maschinelles Lernen; Eingebettete Systeme; Big Data

Set

Published: December 31, 2022
ISBN: 9783111135816