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
Machine learning for evolution strat...
~
Kramer, Oliver.
Machine learning for evolution strategies
紀錄類型:
書目-電子資源 : Monograph/item
正題名/作者:
Machine learning for evolution strategiesby Oliver Kramer.
作者:
Kramer, Oliver.
出版者:
Cham :Springer International Publishing :2016.
面頁冊數:
ix, 124 p. :ill., digital ;24 cm.
Contained By:
Springer eBooks
標題:
Machine learning.
電子資源:
http://dx.doi.org/10.1007/978-3-319-33383-0
ISBN:
9783319333830$q(electronic bk.)
Machine learning for evolution strategies
Kramer, Oliver.
Machine learning for evolution strategies
[electronic resource] /by Oliver Kramer. - Cham :Springer International Publishing :2016. - ix, 124 p. :ill., digital ;24 cm. - Studies in big data,v.202197-6503 ;. - Studies in big data ;v.1..
Part I Evolution Strategies -- Part II Machine Learning -- Part III Supervised Learning.
This book introduces numerous algorithmic hybridizations between both worlds that show how machine learning can improve and support evolution strategies. The set of methods comprises covariance matrix estimation, meta-modeling of fitness and constraint functions, dimensionality reduction for search and visualization of high-dimensional optimization processes, and clustering-based niching. After giving an introduction to evolution strategies and machine learning, the book builds the bridge between both worlds with an algorithmic and experimental perspective. Experiments mostly employ a (1+1)-ES and are implemented in Python using the machine learning library scikit-learn. The examples are conducted on typical benchmark problems illustrating algorithmic concepts and their experimental behavior. The book closes with a discussion of related lines of research.
ISBN: 9783319333830$q(electronic bk.)
Standard No.: 10.1007/978-3-319-33383-0doiSubjects--Topical Terms:
188639
Machine learning.
LC Class. No.: Q325.5
Dewey Class. No.: 006.31
Machine learning for evolution strategies
LDR
:01913nmm a2200325 a 4500
001
489302
003
DE-He213
005
20161020131021.0
006
m d
007
cr nn 008maaau
008
161213s2016 gw s 0 eng d
020
$a
9783319333830$q(electronic bk.)
020
$a
9783319333816$q(paper)
024
7
$a
10.1007/978-3-319-33383-0
$2
doi
035
$a
978-3-319-33383-0
040
$a
GP
$c
GP
041
0
$a
eng
050
4
$a
Q325.5
072
7
$a
UYQ
$2
bicssc
072
7
$a
COM004000
$2
bisacsh
082
0 4
$a
006.31
$2
23
090
$a
Q325.5
$b
.K89 2016
100
1
$a
Kramer, Oliver.
$3
307657
245
1 0
$a
Machine learning for evolution strategies
$h
[electronic resource] /
$c
by Oliver Kramer.
260
$a
Cham :
$b
Springer International Publishing :
$b
Imprint: Springer,
$c
2016.
300
$a
ix, 124 p. :
$b
ill., digital ;
$c
24 cm.
490
1
$a
Studies in big data,
$x
2197-6503 ;
$v
v.20
505
0
$a
Part I Evolution Strategies -- Part II Machine Learning -- Part III Supervised Learning.
520
$a
This book introduces numerous algorithmic hybridizations between both worlds that show how machine learning can improve and support evolution strategies. The set of methods comprises covariance matrix estimation, meta-modeling of fitness and constraint functions, dimensionality reduction for search and visualization of high-dimensional optimization processes, and clustering-based niching. After giving an introduction to evolution strategies and machine learning, the book builds the bridge between both worlds with an algorithmic and experimental perspective. Experiments mostly employ a (1+1)-ES and are implemented in Python using the machine learning library scikit-learn. The examples are conducted on typical benchmark problems illustrating algorithmic concepts and their experimental behavior. The book closes with a discussion of related lines of research.
650
0
$a
Machine learning.
$3
188639
650
1 4
$a
Engineering.
$3
210888
650
2 4
$a
Computational Intelligence.
$3
338479
650
2 4
$a
Simulation and Modeling.
$3
273719
650
2 4
$a
Data Mining and Knowledge Discovery.
$3
275288
650
2 4
$a
Socio- and Econophysics, Population and Evolutionary Models.
$3
495595
650
2 4
$a
Artificial Intelligence (incl. Robotics)
$3
252959
710
2
$a
SpringerLink (Online service)
$3
273601
773
0
$t
Springer eBooks
830
0
$a
Studies in big data ;
$v
v.1.
$3
675357
856
4 0
$u
http://dx.doi.org/10.1007/978-3-319-33383-0
950
$a
Engineering (Springer-11647)
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
000000126813
電子館藏
1圖書
電子書
EB Q325.5 K89 2016
一般使用(Normal)
在架
0
1 筆 • 頁數 1 •
1
多媒體
多媒體檔案
http://dx.doi.org/10.1007/978-3-319-33383-0
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