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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:	Lepore, Antonio.
面頁冊數:	VII, 123 p. 45 illus., 32 illus. in color.online resource. :
Contained By:	Springer Nature eBook
標題:	Statistics . -
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:

1253516
Statistics .

LC Class. No.: QA276-280

Dewey Class. No.: 519.5

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