| 书目名称 | Interpretability for Industry 4.0 : Statistical and Machine Learning Approaches |
| 编辑 | Antonio Lepore,Biagio Palumbo,Jean-Michel Poggi |
| 视频video | |
| 概述 | Gives an introduction to interpretability in statistical and machine learning approaches for Industry 4.0.Provides different views in connection with explainability, generalizability and sensitivity a |
| 图书封面 |  |
| 描述 | 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.. |
| 出版日期 | Book 2022 |
| 关键词 | Generalized Additive Models; Machine Learning; Sensitivity; Additive Manufacturing Systems; Interpretabi |
| 版次 | 1 |
| doi | https://doi.org/10.1007/978-3-031-12402-0 |
| isbn_softcover | 978-3-031-12401-3 |
| isbn_ebook | 978-3-031-12402-0 |
| copyright | The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerl |
发表于 2025-3-21 19:47:03
