Applications of Machine Learning in Large-Scale Optimization and High-Dimensional Learning (Hardcover)
Language: English
Published by Mdpi AG, 2026
- Hardcover
- New

Seller: CitiRetail, Stevenage, United KingdomCitiRetail
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Condition: New
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Hardcover. Machine Learning (ML) has been widely applied to large-scale optimization and high-dimensional learning problems across industries and academic fields, helping solve complex challenges that traditional methods cannot easily address. In large-scale optimization, ML techniques tackle complex tasks involving massive data and variables, such as supply chain logistics optimization, industrial task scheduling, and ML algorithm parameter tuning, delivering more accurate results than conventional approaches. Additionally, ML is well-suited for multi-objective optimization problems-where multiple conflicting goals need simultaneous optimization-common in engineering, finance, and resource allocation. Furthermore, ML efficiently uncovers hidden patterns in high-dimensional data, supporting practical applications like marketing customer segmentation, anomaly detection, and image segmentation. Overall, ML's versatility makes it a key driver in advancing these fields, providing innovative solutions across professions. This item is printed on demand. Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability.…
Seller Inventory # 9783725890477
- Title
- Applications of Machine Learning in Large-Scale Optimization and High-Dimensional Learning (Hardcover)
- Author
- Jeng-Shyang Pan
- Publisher
- Mdpi AG
- Publication year
- 2026
- Condition
- new
- Binding
- Hardcover
- Language
- English
- ISBN 10
- 3725890471
- ISBN 13
- 9783725890477
Machine Learning (ML) has been widely applied to large-scale optimization and high-dimensional learning problems across industries and academic fields, helping solve complex challenges that traditional methods cannot easily address. In large-scale optimization, ML techniques tackle complex tasks involving massive data and variables, such as supply chain logistics optimization, industrial task scheduling, and ML algorithm parameter tuning, delivering more accurate results than conventional approaches. Additionally, ML is well-suited for multi-objective optimization problems-where multiple conflicting goals need simultaneous optimization-common in engineering, finance, and resource allocation. Furthermore, ML efficiently uncovers hidden patterns in high-dimensional data, supporting practical applications like marketing customer segmentation, anomaly detection, and image segmentation. Overall, ML's versatility makes it a key driver in advancing these fields, providing innovative solutions across professions.
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CitiRetail
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