Alternating Direction Method of Multipliers for Machine Learning

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Éditeur :

Springer

Paru le : 2022-06-15

Machine learning heavily relies on optimization algorithms to solve its learning models. Constrained problems constitute a major type of optimization problem, and the alternating direction method of multipliers (ADMM) is a commonly used algorithm to solve constrained problems, especially linearly co...
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Éditeur

Collection
n.c

Parution
2022-06-15

Pages
263 pages

EAN papier
9789811698392

Auteur(s) du livre


Zhouchen Lin is a leading expert in the fields of machine learning and optimization. He is currently a professor with the Key Laboratory of Machine Perception (Ministry of Education), School of Artificial Intelligence, Peking University. Prof. Lin served as an area chair many times for prestigious conferences, including CVPR, ICCV, NIPS/NeurIPS, ICML, ICLR, IJCAI and AAAI. He is a Program Co-Chair of ICPR 2022 and a Senior Area Chair of ICML 2022. Prof. Lin is an associate editor of the International Journal of Computer Vision and the Optimization Methods and Software. He is a Fellow of CSIG, IAPR and IEEE.Huan Li received a doctoral degree in machine learning from Peking University in 2019. He is currently an assistant researcher at the School of Artificial Intelligence, Nankai University. His research interests include optimization and machine learning. Cong Fang received a doctoral degree in machine learning from Peking University in 2019. He is currently anassistant professor at the School of Artificial Intelligence, Peking University. His research interests include optimization and machine learning.

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EAN PDF
9789811698408
Prix
137,14 €
Nombre pages copiables
2
Nombre pages imprimables
26
Taille du fichier
3297 Ko
EAN EPUB
9789811698408
Prix
137,14 €
Nombre pages copiables
2
Nombre pages imprimables
26
Taille du fichier
22047 Ko

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