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Interpretability for Industry 4.0statistical and machine learning approaches /

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Interpretability for Industry 4.0edited by Antonio Lepore, Biagio Palumbo, Jean-Michel Poggi.
其他題名:	statistical and machine learning approaches /
其他作者:	Lepore, Antonio.
出版者:	Cham :Springer International Publishing :2022.
面頁冊數:	vii, 123 p. :ill. (some col.), digital ;24 cm.
Contained By:	Springer Nature eBook
標題:	Industry 4.0.
電子資源:	https://doi.org/10.1007/978-3-031-12402-0
ISBN:	9783031124020$q(electronic bk.)

Interpretability for Industry 4.0statistical 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$q(electronic bk.)

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

884221
Industry 4.0.

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

Dewey Class. No.: 658.4038028563

Interpretability for Industry 4.0statistical and machine learning approaches /
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