國立高雄大學圖資館 |

Language: English

Back

Interpretability of computational in...

Abonyi, Janos.

Interpretability of computational intelligence-based regression models

Record Type:	Electronic resources : Monograph/item
Title/Author:	Interpretability of computational intelligence-based regression modelsby Tamas Kenesei, Janos Abonyi.
Author:	Kenesei, Tamas.
other author:	Abonyi, Janos.
Published:	Cham :Springer International Publishing :2015.
Description:	x, 82 p. :ill. (some col.), digital ;24 cm.
Contained By:	Springer eBooks
Subject:	Regression analysis.
Online resource:	http://dx.doi.org/10.1007/978-3-319-21942-4
ISBN:	9783319219424$q(electronic bk.)

Interpretability of computational intelligence-based regression models
Kenesei, Tamas.

Interpretability of computational intelligence-based regression models[electronic resource] /by Tamas Kenesei, Janos Abonyi. - Cham :Springer International Publishing :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$q(electronic bk.)

Standard No.: 10.1007/978-3-319-21942-4doiSubjects--Topical Terms:

181872
Regression analysis.

LC Class. No.: QA278.2

Dewey Class. No.: 519.536

Interpretability of computational intelligence-based regression models
LDR:02295nmm a2200337 a 4500 001 476493
003 DE-He213
005 20160503113729.0
006 m d
007 cr nn 008maaau
008 160526s2015 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 731163
245 1 0 $a Interpretability of computational intelligence-based regression models $h [electronic resource] / $c by Tamas Kenesei, Janos Abonyi.
260 $a Cham : $b Springer International Publishing : $b Imprint: Springer, $c 2015.
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 0 $a Regression analysis. $3 181872
650 1 4 $a Computer Science. $3 212513
650 2 4 $a Artificial Intelligence (incl. Robotics) $3 252959
650 2 4 $a Computational Intelligence. $3 338479
650 2 4 $a Data Mining and Knowledge Discovery. $3 275288
700 1 $a Abonyi, Janos. $3 731164
710 2 $a SpringerLink (Online service) $3 273601
773 0 $t Springer eBooks
830 0 $a SpringerBriefs in computer science. $3 559641
856 4 0 $u http://dx.doi.org/10.1007/978-3-319-21942-4
950 $a Computer Science (Springer-11645)