Accelerated Optimization for Machine Learning
First-Order Algorithms
(Sprache: Englisch)
This book on optimization includes forewords by Michael I. Jordan, Zongben Xu and Zhi-Quan Luo. Machine learning relies heavily on optimization to solve problems with its learning models, and first-order optimization algorithms are the mainstream...
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Klappentext zu „Accelerated Optimization for Machine Learning “
This book on optimization includes forewords by Michael I. Jordan, Zongben Xu and Zhi-Quan Luo. Machine learning relies heavily on optimization to solve problems with its learning models, and first-order optimization algorithms are the mainstream approaches. The acceleration of first-order optimization algorithms is crucial for the efficiency of machine learning.Written by leading experts in the field, this book provides a comprehensive introduction to, and state-of-the-art review of accelerated first-order optimization algorithms for machine learning. It discusses a variety of methods, including deterministic and stochastic algorithms, where the algorithms can be synchronous or asynchronous, for unconstrained and constrained problems, which can be convex or non-convex. Offering a rich blend of ideas, theories and proofs, the book is up-to-date and self-contained. It is an excellent reference resource for users who are seeking faster optimization algorithms, as well asfor graduate students and researchers wanting to grasp the frontiers of optimization in machine learning in a short time.
Inhaltsverzeichnis zu „Accelerated Optimization for Machine Learning “
Chapter 1. Introduction.- Chapter 2. Accelerated Algorithms for Unconstrained Convex Optimization.- Chapter 3. Accelerated Algorithms for Constrained Convex Optimization.- Chapter 4. Accelerated Algorithms for Nonconvex Optimization.- Chapter 5. Accelerated Stochastic Algorithms.- Chapter 6. Accelerated Paralleling Algorithms.- Chapter 7. Conclusions.-
Autoren-Porträt von Zhouchen Lin, Huan Li, Cong Fang
Zhouchen Lin is a leading expert in the fields of machine learning and computer vision. He is currently a Professor at the Key Laboratory of Machine Perception (Ministry of Education), School of EECS, Peking University. He served as an area chair for several prestigious conferences, including CVPR, ICCV, ICML, NIPS, AAAI and IJCAI. He is an associate editor of the IEEE Transactions on Pattern Analysis and Machine Intelligence and the International Journal of Computer Vision. He is a Fellow of IAPR and IEEE.
Bibliographische Angaben
- Autoren: Zhouchen Lin , Huan Li , Cong Fang
- 2021, 1st ed. 2020, XXIV, 275 Seiten, Masse: 15,5 x 23,5 cm, Kartoniert (TB), Englisch
- Verlag: Springer, Berlin
- ISBN-10: 9811529124
- ISBN-13: 9789811529122
Sprache:
Englisch
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