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Synopsis
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.
Les informations fournies dans la section « Synopsis » peuvent faire référence à une autre édition de ce titre.
À propos de l?auteur
Antonio Lepore is an Associate Professor of Statistics for Experimental and Technological Research (SECS-S/02) in the Department of Industrial Engineering of the University of Naples Federico II.
His research interests and publications in international journals focus on the use of statistical methods for the analysis and monitoring of functional data aimed at the interpretation of complex data coming from high-frequency multi-sensor data acquisition systems.
He is a member of the ENBIS (European Network for Business and Industrial Statistics) and SIS (the Italian Statistical Society).
Biagio Palumbo is an Associate Professor of Statistics for Experimental and Technological Research (SECS-S/02) in the Department of Industrial Engineering of the University of Naples Federico II and President Elect of the European Network for Business and Industrial Statistics (ENBIS).
His research interests are in interpretable statistical learning techniques for industrial engineering and, in particular, for the monitoring of complex data coming from high-frequency multi-sensor acquisition systems and for optimization of manufacturing processes.
He is member of the Italian Statistical Society, the American Society for Quality (ASQ), and the Italian Association of Mechanical Technology.
Jean-Michel Poggi is a Professor of Statistics at Université Paris Cité and a member of the Lab. Maths Orsay (LMO) at Université Paris-Saclay, in France.
His research interests are in nonparametric time series, wavelets, tree-based methods (CART, Random Forests, Boosting) and applied statistics. His work combines theoretical and practical contributions with industrial applications (mainly environment and energy) and software development.
He is Associate Editor of three journals: the Journal of Statistical Software (JSS), Advances in Data Analysis and Classification (ADAC) and the Journal of Data Science, Statistics, and Visualisation (JDSSV).
He is President of the European Network for Business and Industrial Statistics (ENBIS).
Les informations fournies dans la section « A propos du livre » peuvent faire référence à une autre édition de ce titre.
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Interpretability for Industry 4.0 : Statistical and Machine Learning Approaches
Edité par
Springer International Publishing, 2022
ISBN 10 : 3031124014
ISBN 13 : 9783031124013
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Interpretability for Industry 4.0 : Statistical and Machine Learning Approaches
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Etat : New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. 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 indu. N° de réf. du vendeur 668447235
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Interpretability for Industry 4.0 : Statistical and Machine Learning Approaches
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ISBN 10 : 3031124014
ISBN 13 : 9783031124013
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Taschenbuch. Etat : Neu. Druck auf Anfrage Neuware - Printed after ordering - 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. N° de réf. du vendeur 9783031124013
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Interpretability for Industry 4.0 : Statistical and Machine Learning Approaches
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ISBN 10 : 3031124014
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Taschenbuch. Etat : Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -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. 132 pp. Englisch. N° de réf. du vendeur 9783031124013
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Interpretability for Industry 4.0 : Statistical and Machine Learning Approaches
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Interpretability for Industry 4.0 : Statistical and Machine Learning Approaches
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Interpretability for Industry 4.0 : Statistical and Machine Learning Approaches
ISBN 10 : 3031124014
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Taschenbuch. Etat : Neu. Neuware -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.Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg 132 pp. Englisch. N° de réf. du vendeur 9783031124013
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Interpretability for Industry 4.0: Statistical and Machine Learning Approaches
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