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First-order and stochastic optimizat...

Lan, Guanghui.

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First-order and stochastic optimization methods for machine learning

Record Type:	Electronic resources : Monograph/item
Title/Author:	First-order and stochastic optimization methods for machine learning/ by Guanghui Lan.
Author:	Lan, Guanghui.
Published:	Cham :Springer International Publishing : : 2020.,
Description:	xiii, 582 p. :ill., digital ;24 cm.
[NT 15003449]:	Machine Learning Models -- Convex Optimization Theory -- Deterministic Convex Optimization -- Stochastic Convex Optimization -- Convex Finite-sum and Distributed Optimization -- Nonconvex Optimization -- Projection-free Methods -- Operator Sliding and Decentralized Optimization.
Contained By:	Springer eBooks
Subject:	Mathematical optimization. -
Online resource:	https://doi.org/10.1007/978-3-030-39568-1
ISBN:	9783030395681

First-order and stochastic optimization methods for machine learning
Lan, Guanghui.

First-order and stochastic optimization methods for machine learning[electronic resource] /by Guanghui Lan. - Cham :Springer International Publishing :2020. - xiii, 582 p. :ill., digital ;24 cm. - Springer series in the data sciences,2365-5674. - Springer series in the data sciences..

Machine Learning Models -- Convex Optimization Theory -- Deterministic Convex Optimization -- Stochastic Convex Optimization -- Convex Finite-sum and Distributed Optimization -- Nonconvex Optimization -- Projection-free Methods -- Operator Sliding and Decentralized Optimization.

This book covers not only foundational materials but also the most recent progresses made during the past few years on the area of machine learning algorithms. In spite of the intensive research and development in this area, there does not exist a systematic treatment to introduce the fundamental concepts and recent progresses on machine learning algorithms, especially on those based on stochastic optimization methods, randomized algorithms, nonconvex optimization, distributed and online learning, and projection free methods. This book will benefit the broad audience in the area of machine learning, artificial intelligence and mathematical programming community by presenting these recent developments in a tutorial style, starting from the basic building blocks to the most carefully designed and complicated algorithms for machine learning.

ISBN: 9783030395681

Standard No.: 10.1007/978-3-030-39568-1doiSubjects--Topical Terms:

517763
Mathematical optimization.

LC Class. No.: QA402.5 / .L364 2020

Dewey Class. No.: 519.6

First-order and stochastic optimization methods for machine learning
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520 $a This book covers not only foundational materials but also the most recent progresses made during the past few years on the area of machine learning algorithms. In spite of the intensive research and development in this area, there does not exist a systematic treatment to introduce the fundamental concepts and recent progresses on machine learning algorithms, especially on those based on stochastic optimization methods, randomized algorithms, nonconvex optimization, distributed and online learning, and projection free methods. This book will benefit the broad audience in the area of machine learning, artificial intelligence and mathematical programming community by presenting these recent developments in a tutorial style, starting from the basic building blocks to the most carefully designed and complicated algorithms for machine learning.
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