東華大學圖書館 |

Language: English

Help

回圖書館首頁

手機版館藏查詢

Back

Switch To: Labeled | MARC Mode | ISBD

Interpretability for Industry 4.0 = ...

Lepore, Antonio.

Linked to FindBook

Google Book

Amazon

博客來

Interpretability for Industry 4.0 = statistical and machine learning approaches /

Record Type:	Electronic resources : Monograph/item
Title/Author:	Interpretability for Industry 4.0/ edited by Antonio Lepore, Biagio Palumbo, Jean-Michel Poggi.
Reminder of title:	statistical and machine learning approaches /
other author:	Lepore, Antonio.
Published:	Cham :Springer International Publishing : : 2022.,
Description:	vii, 123 p. :ill. (some col.), digital ;24 cm.
Contained By:	Springer Nature eBook
Subject:	Industry 4.0. -
Online resource:	https://doi.org/10.1007/978-3-031-12402-0
ISBN:	9783031124020

Interpretability for Industry 4.0 = statistical and machine learning approaches /
Interpretability for Industry 4.0statistical and machine learning approaches /[electronic resource] :edited by Antonio Lepore, Biagio Palumbo, Jean-Michel Poggi. - Cham :Springer International Publishing :2022. - vii, 123 p. :ill. (some col.), digital ;24 cm.

This volume provides readers with a compact, stimulating and multifaceted introduction to interpretability, a key issue for developing insightful statistical and machine learning approaches as well as for communicating modelling results in business and industry. Different views in the context of Industry 4.0 are offered in connection with the concepts of explainability of machine learning tools, generalizability of model outputs and sensitivity analysis. Moreover, the book explores the integration of Artificial Intelligence and robust analysis of variance for big data mining and monitoring in Additive Manufacturing, and sheds new light on interpretability via random forests and flexible generalized additive models together with related software resources and real-world examples.

ISBN: 9783031124020

Standard No.: 10.1007/978-3-031-12402-0doiSubjects--Topical Terms:

3491401
Industry 4.0.

LC Class. No.: T59.6 / .I57 2022

Dewey Class. No.: 658.4038028563

Interpretability for Industry 4.0 = statistical and machine learning approaches /
LDR:01838nmm a2200313 a 4500 001 2305066
003 DE-He213
005 20221019064410.0
006 m d
007 cr nn 008maaau
008 230409s2022 sz s 0 eng d
020 $a 9783031124020 $q (electronic bk.)
020 $a 9783031124013 $q (paper)
024 7 $a 10.1007/978-3-031-12402-0 $2 doi
035 $a 978-3-031-12402-0
040 $a GP $c GP
041 0 $a eng
050 4 $a T59.6 $b .I57 2022
072 7 $a PBT $2 bicssc
072 7 $a MAT029000 $2 bisacsh
072 7 $a PBT $2 thema
082 0 4 $a 658.4038028563 $2 23
090 $a T59.6 $b .I61 2022
245 0 0 $a Interpretability for Industry 4.0 $h [electronic resource] : $b statistical and machine learning approaches / $c edited by Antonio Lepore, Biagio Palumbo, Jean-Michel Poggi.
260 $a Cham : $b Springer International Publishing : $b Imprint: Springer, $c 2022.
300 $a vii, 123 p. : $b ill. (some col.), digital ; $c 24 cm.
520 $a This volume provides readers with a compact, stimulating and multifaceted introduction to interpretability, a key issue for developing insightful statistical and machine learning approaches as well as for communicating modelling results in business and industry. Different views in the context of Industry 4.0 are offered in connection with the concepts of explainability of machine learning tools, generalizability of model outputs and sensitivity analysis. Moreover, the book explores the integration of Artificial Intelligence and robust analysis of variance for big data mining and monitoring in Additive Manufacturing, and sheds new light on interpretability via random forests and flexible generalized additive models together with related software resources and real-world examples.
650 0 $a Industry 4.0. $3 3491401
650 0 $a Machine learning $x Industrial applications. $3 1361578
650 0 $a Industry 4.0 $x Statistical methods. $3 3607830
650 1 4 $a Statistical Theory and Methods. $3 891074
650 2 4 $a Statistics in Engineering, Physics, Computer Science, Chemistry and Earth Sciences. $3 3591853
650 2 4 $a Statistics in Business, Management, Economics, Finance, Insurance. $3 3538572
700 1 $a Lepore, Antonio. $3 3607828
700 1 $a Palumbo, Biagio. $3 3607829
700 1 $a Poggi, Jean-Michel. $3 2156837
710 2 $a SpringerLink (Online service) $3 836513
773 0 $t Springer Nature eBook
856 4 0 $u https://doi.org/10.1007/978-3-031-12402-0
950 $a Mathematics and Statistics (SpringerNature-11649)