國立虎尾科技大學 |

Interpretability for Industry 4.0 : Statistical and Machine Learning Approaches

紀錄類型:	書目-語言資料,印刷品 : Monograph/item
正題名/作者:	Interpretability for Industry 4.0 : Statistical and Machine Learning Approaches / edited by Antonio Lepore, Biagio Palumbo, Jean-Michel Poggi.
其他作者:	Poggi, Jean-Michel.
面頁冊數:	VII, 123 p. 45 illus., 32 illus. in color.online resource. :
Contained By:	Springer Nature eBook
標題:	Statistics in Business, Management, Economics, Finance, Insurance. -
電子資源:	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 4.0 : Statistical and Machine Learning Approaches [electronic resource] /edited by Antonio Lepore, Biagio Palumbo, Jean-Michel Poggi. - 1st ed. 2022. - VII, 123 p. 45 illus., 32 illus. in color.online resource.

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:

1366003
Statistics in Business, Management, Economics, Finance, Insurance.

LC Class. No.: QA276-280

Dewey Class. No.: 519.5

Interpretability for Industry 4.0 : Statistical and Machine Learning Approaches
LDR:02155nam a22003735i 4500 001 1084437
003 DE-He213
005 20221019064410.0
007 cr nn 008mamaa
008 221228s2022 sz | s |||| 0|eng d
020 $a 9783031124020 $9 978-3-031-12402-0
024 7 $a 10.1007/978-3-031-12402-0 $2 doi
035 $a 978-3-031-12402-0
050 4 $a QA276-280
072 7 $a PBT $2 bicssc
072 7 $a MAT029000 $2 bisacsh
072 7 $a PBT $2 thema
082 0 4 $a 519.5 $2 23
245 1 0 $a Interpretability for Industry 4.0 : Statistical and Machine Learning Approaches $h [electronic resource] / $c edited by Antonio Lepore, Biagio Palumbo, Jean-Michel Poggi.
250 $a 1st ed. 2022.
264 1 $a Cham : $b Springer International Publishing : $b Imprint: Springer, $c 2022.
300 $a VII, 123 p. 45 illus., 32 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
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 2 4 $a Statistics in Business, Management, Economics, Finance, Insurance. $3 1366003
650 2 4 $a Statistics in Engineering, Physics, Computer Science, Chemistry and Earth Sciences. $3 1366002
650 1 4 $a Statistical Theory and Methods. $3 671396
650 0 $a Statistics . $3 1253516
700 1 $a Poggi, Jean-Michel. $e editor. $4 edt $4 http://id.loc.gov/vocabulary/relators/edt $3 1253513
700 1 $a Palumbo, Biagio. $e editor. $4 edt $4 http://id.loc.gov/vocabulary/relators/edt $3 1390714
700 1 $a Lepore, Antonio. $e editor. $4 edt $4 http://id.loc.gov/vocabulary/relators/edt $3 1390713
710 2 $a SpringerLink (Online service) $3 593884
773 0 $t Springer Nature eBook
776 0 8 $i Printed edition: $z 9783031124013
776 0 8 $i Printed edition: $z 9783031124037
856 4 0 $u https://doi.org/10.1007/978-3-031-12402-0
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)