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8 matches in All Departments
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 as for graduate students and researchers wanting to grasp
the frontiers of optimization in machine learning in a short time.
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 as for graduate students and researchers wanting to grasp
the frontiers of optimization in machine learning in a short time.
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 constrained ones. Written
by experts in machine learning and optimization, this is the first
book providing a state-of-the-art review on ADMM under various
scenarios, including deterministic and convex optimization,
nonconvex optimization, stochastic optimization, and distributed
optimization. Offering a rich blend of ideas, theories and proofs,
the book is up-to-date and self-contained. It is an excellent
reference book for users who are seeking a relatively universal
algorithm for constrained problems. Graduate students or
researchers can read it to grasp the frontiers of ADMM in machine
learning in a short period of time.
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 constrained ones. Written
by experts in machine learning and optimization, this is the first
book providing a state-of-the-art review on ADMM under various
scenarios, including deterministic and convex optimization,
nonconvex optimization, stochastic optimization, and distributed
optimization. Offering a rich blend of ideas, theories and proofs,
the book is up-to-date and self-contained. It is an excellent
reference book for users who are seeking a relatively universal
algorithm for constrained problems. Graduate students or
researchers can read it to grasp the frontiers of ADMM in machine
learning in a short period of time.
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Pattern Recognition and Computer Vision - Second Chinese Conference, PRCV 2019, Xi'an, China, November 8-11, 2019, Proceedings, Part I (Paperback, 1st ed. 2019)
Zhouchen Lin, Liang Wang, Jian Yang, Guangming Shi, Tieniu Tan, …
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R1,672
Discovery Miles 16 720
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Ships in 10 - 15 working days
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The three-volume set LNCS 11857, 11858, and 11859 constitutes the
refereed proceedings of the Second Chinese Conference on Pattern
Recognition and Computer Vision, PRCV 2019, held in Xi'an, China,
in November 2019. The 165 revised full papers presented were
carefully reviewed and selected from 412 submissions. The papers
have been organized in the following topical sections: Part I:
Object Detection, Tracking and Recognition, Part II: Image/Video
Processing and Analysis, Part III: Data Analysis and Optimization.
Low-Rank Models in Visual Analysis: Theories, Algorithms, and
Applications presents the state-of-the-art on low-rank models and
their application to visual analysis. It provides insight into the
ideas behind the models and their algorithms, giving details of
their formulation and deduction. The main applications included are
video denoising, background modeling, image alignment and
rectification, motion segmentation, image segmentation and image
saliency detection. Readers will learn which Low-rank models are
highly useful in practice (both linear and nonlinear models), how
to solve low-rank models efficiently, and how to apply low-rank
models to real problems.
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Pattern Recognition and Computer Vision - Second Chinese Conference, PRCV 2019, Xi'an, China, November 8-11, 2019, Proceedings, Part II (Paperback, 1st ed. 2019)
Zhouchen Lin, Liang Wang, Jian Yang, Guangming Shi, Tieniu Tan, …
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R1,727
Discovery Miles 17 270
|
Ships in 10 - 15 working days
|
The three-volume set LNCS 11857, 11858, and 11859 constitutes the
refereed proceedings of the Second Chinese Conference on Pattern
Recognition and Computer Vision, PRCV 2019, held in Xi'an, China,
in November 2019. The 165 revised full papers presented were
carefully reviewed and selected from 412 submissions. The papers
have been organized in the following topical sections: Part I:
Object Detection, Tracking and Recognition, Part II: Image/Video
Processing and Analysis, Part III: Data Analysis and Optimization.
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Pattern Recognition and Computer Vision - Second Chinese Conference, PRCV 2019, Xi'an, China, November 8-11, 2019, Proceedings, Part III (Paperback, 1st ed. 2019)
Zhouchen Lin, Liang Wang, Jian Yang, Guangming Shi, Tieniu Tan, …
|
R1,642
Discovery Miles 16 420
|
Ships in 10 - 15 working days
|
The three-volume set LNCS 11857, 11858, and 11859 constitutes the
refereed proceedings of the Second Chinese Conference on Pattern
Recognition and Computer Vision, PRCV 2019, held in Xi'an, China,
in November 2019. The 165 revised full papers presented were
carefully reviewed and selected from 412 submissions. The papers
have been organized in the following topical sections: Part I:
Object Detection, Tracking and Recognition, Part II: Image/Video
Processing and Analysis, Part III: Data Analysis and Optimization.
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