國立高雄大學圖資館 |

Language: English

Back

First-order and stochastic optimizat...

Lan, Guanghui.

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 learningby Guanghui Lan.
Author:	Lan, Guanghui.
Published:	Cham :Springer International Publishing :2020.
Description:	xiii, 582 p. :ill., digital ;24 cm.
Contained By:	Springer eBooks
Subject:	Mathematical optimization.
Online resource:	https://doi.org/10.1007/978-3-030-39568-1
ISBN:	9783030395681$q(electronic bk.)

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$q(electronic bk.)

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

183292
Mathematical optimization.

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

Dewey Class. No.: 519.6

First-order and stochastic optimization methods for machine learning
LDR:02120nmm a2200337 a 4500 001 579842
003 DE-He213
005 20201007140825.0
006 m
007 cr
008 201229s2020
020 $a 9783030395681$q(electronic bk.)
020 $a 9783030395674$q(paper)
024 7 $a 10.1007/978-3-030-39568-1 $2 doi
035 $a 978-3-030-39568-1
040 $a GP $c GP
041 0 $a eng
050 4 $a QA402.5 $b .L364 2020
072 7 $a PBU $2 bicssc
072 7 $a MAT003000 $2 bisacsh
072 7 $a PBU $2 thema
082 0 4 $a 519.6 $2 23
090 $a QA402.5 $b .L243 2020
100 1 $a Lan, Guanghui. $3 869332
245 1 0 $a First-order and stochastic optimization methods for machine learning $h [electronic resource] / $c by Guanghui Lan.
260 $a Cham : $b Springer International Publishing : $b Imprint: Springer, $c 2020.
300 $a xiii, 582 p. : $b ill., digital ; $c 24 cm.
490 1 $a Springer series in the data sciences, $x 2365-5674
505 0 $a 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.
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.
650 0 $a Mathematical optimization. $3 183292
650 0 $a Machine learning. $3 188639
650 1 4 $a Optimization. $3 274084
650 2 4 $a Machine Learning. $3 833608
710 2 $a SpringerLink (Online service) $3 273601
773 0 $t Springer eBooks
830 0 $a Springer series in the data sciences. $3 732743
856 4 0 $u https://doi.org/10.1007/978-3-030-39568-1
950 $a Mathematics and Statistics (Springer-11649)