語系:
繁體中文
English
說明(常見問題)
圖資館首頁
登入
回首頁
切換:
標籤
|
MARC模式
|
ISBD
First-order and stochastic optimizat...
~
Lan, Guanghui.
First-order and stochastic optimization methods for machine learning
紀錄類型:
書目-電子資源 : Monograph/item
正題名/作者:
First-order and stochastic optimization methods for machine learningby Guanghui Lan.
作者:
Lan, Guanghui.
出版者:
Cham :Springer International Publishing :2020.
面頁冊數:
xiii, 582 p. :ill., digital ;24 cm.
Contained By:
Springer eBooks
標題:
Mathematical optimization.
電子資源:
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)
筆 0 讀者評論
全部
電子館藏
館藏
1 筆 • 頁數 1 •
1
條碼號
館藏地
館藏流通類別
資料類型
索書號
使用類型
借閱狀態
預約狀態
備註欄
附件
000000184428
電子館藏
1圖書
電子書
EB QA402.5 .L243 2020 2020
一般使用(Normal)
在架
0
1 筆 • 頁數 1 •
1
多媒體
多媒體檔案
https://doi.org/10.1007/978-3-030-39568-1
評論
新增評論
分享你的心得
Export
取書館別
處理中
...
變更密碼
登入