Machine Learning under Resource Constraints - Applications

Machine Learning under Resource Constraints explores novel algorithms that handle high-throughput data, high dimensions, or complex structures, emphasizing the role of modern computer architectures in optimizing resource consumption. It provides a comprehensive overview of methods and techniques for solving real-world problems in domains like health, manufacturing, traffic, logistics, smart cities, and mobile communications, with specific application examples.

Format: Paperback / softback
Length: 478 pages
Publication date: 31 December 2022
Publisher: De Gruyter

Machine Learning under Resource Constraints is a comprehensive three-volume set that delves into innovative machine learning algorithms that face the challenges of high-throughput data, high dimensions, and complex data structures. These challenges are exacerbated by resource limitations, which are determined by the interplay between the demands of data processing and the capabilities of computing machinery. The resources at stake include runtime, memory, communication, and energy. Consequently, modern computer architectures play a pivotal role in addressing these constraints.

The book aims to optimize novel machine learning algorithms for minimal resource consumption. It also emphasizes the execution of learned predictions on diverse architectures to further conserve resources. This comprehensive overview provides insights into the latest approaches to machine learning research that take resource constraints into account, as well as their practical applications across various scientific and engineering domains.

Volume 3 specifically focuses on the practical application of resource-aware machine learning methods and techniques to solve real-world problems. It offers numerous specific application examples, showcasing the transformative impact of machine learning in fields such as health and medicine. By improving risk modeling, diagnosis, and treatment selection for diseases, machine learning contributes to better healthcare outcomes. In manufacturing, it enables quality control, reducing material and energy costs while optimizing testing times. Real-time applications in electronics, steel production, milling, traffic management, logistics, smart cities, and mobile communications demonstrate the wide-ranging benefits of machine learning in optimizing efficiency, sustainability, and performance.

In conclusion, Machine Learning under Resource Constraints is an essential resource for researchers, practitioners, and students in the field of machine learning. It provides a comprehensive understanding of the challenges posed by resource constraints and offers innovative solutions to optimize machine learning algorithms for efficient and effective performance. Through its application examples and practical insights, the book demonstrates the immense potential of machine learning to revolutionize various industries and improve our daily lives.

Weight: 798g
Dimension: 240 x 170 (mm)
ISBN-13: 9783110785975