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
Interpretability of computational in...
~
Abonyi, Janos.
Interpretability of computational intelligence-based regression models
紀錄類型:
書目-語言資料,印刷品 : Monograph/item
正題名/作者:
Interpretability of computational intelligence-based regression models/ by Tamas Kenesei, Janos Abonyi.
作者:
Kenesei, Tamas.
其他作者:
Abonyi, Janos.
出版者:
Cham :Imprint: Springer, : 2015.,
面頁冊數:
x, 82 p. :ill. (some col.), digital ; : 24 cm.;
Contained By:
Springer eBooks
標題:
Data Mining and Knowledge Discovery. -
電子資源:
http://dx.doi.org/10.1007/978-3-319-21942-4
ISBN:
9783319219424
Interpretability of computational intelligence-based regression models
Kenesei, Tamas.
Interpretability of computational intelligence-based regression models
[electronic resource] /by Tamas Kenesei, Janos Abonyi. - Cham :Imprint: Springer,2015. - x, 82 p. :ill. (some col.), digital ;24 cm. - SpringerBriefs in computer science,2191-5768. - SpringerBriefs in computer science..
Introduction -- Interpretability of Hinging Hyperplanes -- Interpretability of Neural Networks -- Interpretability of Support Vector Machines -- Summary.
The key idea of this book is that hinging hyperplanes, neural networks and support vector machines can be transformed into fuzzy models, and interpretability of the resulting rule-based systems can be ensured by special model reduction and visualization techniques. The first part of the book deals with the identification of hinging hyperplane-based regression trees. The next part deals with the validation, visualization and structural reduction of neural networks based on the transformation of the hidden layer of the network into an additive fuzzy rule base system. Finally, based on the analogy of support vector regression and fuzzy models, a three-step model reduction algorithm is proposed to get interpretable fuzzy regression models on the basis of support vector regression. The authors demonstrate real-world use of the algorithms with examples taken from process engineering, and they support the text with downloadable Matlab code. The book is suitable for researchers, graduate students and practitioners in the areas of computational intelligence and machine learning.
ISBN: 9783319219424
Standard No.: 10.1007/978-3-319-21942-4doiSubjects--Topical Terms:
677765
Data Mining and Knowledge Discovery.
LC Class. No.: QA278.2
Dewey Class. No.: 519.536
Interpretability of computational intelligence-based regression models
LDR
:02295nam a2200337 a 4500
001
838166
003
DE-He213
005
20160503113729.0
006
m d
007
cr nn 008maaau
008
160616s2015 gw s 0 eng d
020
$a
9783319219424
$q
(electronic bk.)
020
$a
9783319219417
$q
(paper)
024
7
$a
10.1007/978-3-319-21942-4
$2
doi
035
$a
978-3-319-21942-4
040
$a
GP
$c
GP
041
0
$a
eng
050
4
$a
QA278.2
072
7
$a
UYQ
$2
bicssc
072
7
$a
TJFM1
$2
bicssc
072
7
$a
COM004000
$2
bisacsh
082
0 4
$a
519.536
$2
23
090
$a
QA278.2
$b
.K33 2015
100
1
$a
Kenesei, Tamas.
$3
1069165
245
1 0
$a
Interpretability of computational intelligence-based regression models
$h
[electronic resource] /
$c
by Tamas Kenesei, Janos Abonyi.
260
$a
Cham :
$c
2015.
$b
Imprint: Springer,
$b
Springer International Publishing :
300
$a
x, 82 p. :
$b
ill. (some col.), digital ;
$c
24 cm.
490
1
$a
SpringerBriefs in computer science,
$x
2191-5768
505
0
$a
Introduction -- Interpretability of Hinging Hyperplanes -- Interpretability of Neural Networks -- Interpretability of Support Vector Machines -- Summary.
520
$a
The key idea of this book is that hinging hyperplanes, neural networks and support vector machines can be transformed into fuzzy models, and interpretability of the resulting rule-based systems can be ensured by special model reduction and visualization techniques. The first part of the book deals with the identification of hinging hyperplane-based regression trees. The next part deals with the validation, visualization and structural reduction of neural networks based on the transformation of the hidden layer of the network into an additive fuzzy rule base system. Finally, based on the analogy of support vector regression and fuzzy models, a three-step model reduction algorithm is proposed to get interpretable fuzzy regression models on the basis of support vector regression. The authors demonstrate real-world use of the algorithms with examples taken from process engineering, and they support the text with downloadable Matlab code. The book is suitable for researchers, graduate students and practitioners in the areas of computational intelligence and machine learning.
650
2 4
$a
Data Mining and Knowledge Discovery.
$3
677765
650
2 4
$a
Computational Intelligence.
$3
768837
650
2 4
$a
Artificial Intelligence (incl. Robotics)
$3
593924
650
1 4
$a
Computer Science.
$3
593922
650
0
$a
Regression analysis.
$3
569541
700
1
$a
Abonyi, Janos.
$3
1069166
710
2
$a
SpringerLink (Online service)
$3
593884
773
0
$t
Springer eBooks
830
0
$a
SpringerBriefs in computer science.
$3
883114
856
4 0
$u
http://dx.doi.org/10.1007/978-3-319-21942-4
950
$a
Computer Science (Springer-11645)
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