語系:
繁體中文
English
說明(常見問題)
登入
回首頁
切換:
標籤
|
MARC模式
|
ISBD
Black Box Optimization, Machine Lear...
~
Vrahatis, Michael N.
Black Box Optimization, Machine Learning, and No-Free Lunch Theorems
紀錄類型:
書目-語言資料,印刷品 : Monograph/item
正題名/作者:
Black Box Optimization, Machine Learning, and No-Free Lunch Theorems/ edited by Panos M. Pardalos, Varvara Rasskazova, Michael N. Vrahatis.
其他作者:
Vrahatis, Michael N.
面頁冊數:
X, 388 p. 113 illus., 90 illus. in color.online resource. :
Contained By:
Springer Nature eBook
標題:
Machine Learning. -
電子資源:
https://doi.org/10.1007/978-3-030-66515-9
ISBN:
9783030665159
Black Box Optimization, Machine Learning, and No-Free Lunch Theorems
Black Box Optimization, Machine Learning, and No-Free Lunch Theorems
[electronic resource] /edited by Panos M. Pardalos, Varvara Rasskazova, Michael N. Vrahatis. - 1st ed. 2021. - X, 388 p. 113 illus., 90 illus. in color.online resource. - Springer Optimization and Its Applications,1701931-6828 ;. - Springer Optimization and Its Applications,104.
Learning enabled constrained black box optimization (Archetti) -- Black-box optimization: Methods and applications (Hasan) -- Tuning algorithms for stochastic black-box optimization: State of the art and future perspectives (Bartz-Beielstein) -- Quality diversity optimization: A novel branch of stochastic optimization (Chatzilygeroudis) -- Multi-objective evolutionary algorithms: Past, present and future (Coello C.A) -- Black-box and data driven computation (Du) -- Mathematically rigorous global optimization and fuzzy optimization: A brief comparison of paradigms, methods, similarities and differences (Kearfott) -- Optimization under Uncertainty Explains Empirical Success of Deep Learning Heuristics (Kreinovich) -- Variable neighborhood programming as a tool of machine learning (Mladenovic) -- Non-lattice covering and quanitization of high dimensional sets (Zhigljavsky) -- Finding effective SAT partitionings via black-box optimization (Semenov) -- The No Free Lunch Theorem: What are its main implications for the optimization practice? ( Serafino) -- What is important about the No Free Lunch theorems? (Wolpert).
This edited volume illustrates the connections between machine learning techniques, black box optimization, and no-free lunch theorems. Each of the thirteen contributions focuses on the commonality and interdisciplinary concepts as well as the fundamentals needed to fully comprehend the impact of individual applications and problems. Current theoretical, algorithmic, and practical methods used are provided to stimulate a new effort towards innovative and efficient solutions. The book is intended for beginners who wish to achieve a broad overview of optimization methods and also for more experienced researchers as well as researchers in mathematics, optimization, operations research, quantitative logistics, data analysis, and statistics, who will benefit from access to a quick reference to key topics and methods. The coverage ranges from mathematically rigorous methods to heuristic and evolutionary approaches in an attempt to equip the reader with different viewpoints of the same problem.
ISBN: 9783030665159
Standard No.: 10.1007/978-3-030-66515-9doiSubjects--Topical Terms:
1137723
Machine Learning.
LC Class. No.: QA402.5-402.6
Dewey Class. No.: 519.6
Black Box Optimization, Machine Learning, and No-Free Lunch Theorems
LDR
:03638nam a22004095i 4500
001
1054845
003
DE-He213
005
20210707132720.0
007
cr nn 008mamaa
008
220103s2021 gw | s |||| 0|eng d
020
$a
9783030665159
$9
978-3-030-66515-9
024
7
$a
10.1007/978-3-030-66515-9
$2
doi
035
$a
978-3-030-66515-9
050
4
$a
QA402.5-402.6
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
245
1 0
$a
Black Box Optimization, Machine Learning, and No-Free Lunch Theorems
$h
[electronic resource] /
$c
edited by Panos M. Pardalos, Varvara Rasskazova, Michael N. Vrahatis.
250
$a
1st ed. 2021.
264
1
$a
Cham :
$b
Springer International Publishing :
$b
Imprint: Springer,
$c
2021.
300
$a
X, 388 p. 113 illus., 90 illus. in color.
$b
online resource.
336
$a
text
$b
txt
$2
rdacontent
337
$a
computer
$b
c
$2
rdamedia
338
$a
online resource
$b
cr
$2
rdacarrier
347
$a
text file
$b
PDF
$2
rda
490
1
$a
Springer Optimization and Its Applications,
$x
1931-6828 ;
$v
170
505
0
$a
Learning enabled constrained black box optimization (Archetti) -- Black-box optimization: Methods and applications (Hasan) -- Tuning algorithms for stochastic black-box optimization: State of the art and future perspectives (Bartz-Beielstein) -- Quality diversity optimization: A novel branch of stochastic optimization (Chatzilygeroudis) -- Multi-objective evolutionary algorithms: Past, present and future (Coello C.A) -- Black-box and data driven computation (Du) -- Mathematically rigorous global optimization and fuzzy optimization: A brief comparison of paradigms, methods, similarities and differences (Kearfott) -- Optimization under Uncertainty Explains Empirical Success of Deep Learning Heuristics (Kreinovich) -- Variable neighborhood programming as a tool of machine learning (Mladenovic) -- Non-lattice covering and quanitization of high dimensional sets (Zhigljavsky) -- Finding effective SAT partitionings via black-box optimization (Semenov) -- The No Free Lunch Theorem: What are its main implications for the optimization practice? ( Serafino) -- What is important about the No Free Lunch theorems? (Wolpert).
520
$a
This edited volume illustrates the connections between machine learning techniques, black box optimization, and no-free lunch theorems. Each of the thirteen contributions focuses on the commonality and interdisciplinary concepts as well as the fundamentals needed to fully comprehend the impact of individual applications and problems. Current theoretical, algorithmic, and practical methods used are provided to stimulate a new effort towards innovative and efficient solutions. The book is intended for beginners who wish to achieve a broad overview of optimization methods and also for more experienced researchers as well as researchers in mathematics, optimization, operations research, quantitative logistics, data analysis, and statistics, who will benefit from access to a quick reference to key topics and methods. The coverage ranges from mathematically rigorous methods to heuristic and evolutionary approaches in an attempt to equip the reader with different viewpoints of the same problem.
650
2 4
$a
Machine Learning.
$3
1137723
650
1 4
$a
Optimization.
$3
669174
650
0
$a
Machine learning.
$3
561253
650
0
$a
Mathematical optimization.
$3
527675
700
1
$a
Vrahatis, Michael N.
$e
editor.
$4
edt
$4
http://id.loc.gov/vocabulary/relators/edt
$3
1359983
700
1
$a
Rasskazova, Varvara.
$e
editor.
$4
edt
$4
http://id.loc.gov/vocabulary/relators/edt
$3
1359982
700
1
$a
Pardalos, Panos M.
$4
edt
$4
http://id.loc.gov/vocabulary/relators/edt
$3
669384
710
2
$a
SpringerLink (Online service)
$3
593884
773
0
$t
Springer Nature eBook
776
0 8
$i
Printed edition:
$z
9783030665142
776
0 8
$i
Printed edition:
$z
9783030665166
776
0 8
$i
Printed edition:
$z
9783030665173
830
0
$a
Springer Optimization and Its Applications,
$x
1931-6828 ;
$v
104
$3
1255232
856
4 0
$u
https://doi.org/10.1007/978-3-030-66515-9
912
$a
ZDB-2-SMA
912
$a
ZDB-2-SXMS
950
$a
Mathematics and Statistics (SpringerNature-11649)
950
$a
Mathematics and Statistics (R0) (SpringerNature-43713)
筆 0 讀者評論
多媒體
評論
新增評論
分享你的心得
Export
取書館別
處理中
...
變更密碼[密碼必須為2種組合(英文和數字)及長度為10碼以上]
登入