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  • 1
    Online Resource
    Online Resource
    Interpretability for Industry 4.0 : Statistical and Machine Learning Approaches (2022)
    Cham :Springer International Publishing :
    Details
    UID:
    almahu_9949387844402882
    Format: VII, 123 p. 45 illus., 32 illus. in color. , online resource.
    Edition: 1st ed. 2022.
    ISBN: 9783031124020
    Content: 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.
    In: Springer Nature eBook
    Additional Edition: Printed edition: ISBN 9783031124013
    Additional Edition: Printed edition: ISBN 9783031124037
    Language: English
    DOI: 10.1007/978-3-031-12402-0
    URL: https://doi.org/10.1007/978-3-031-12402-0
    URL: lizenzpflichtig
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    HU Berlin
    UB Potsdam
  • 2
    Online Resource
    Online Resource
    Interpretability for Industry 4.0 : Statistical and Machine Learning Approaches (2022)
    Cham : Springer International Publishing | Cham : Springer
    Details
    UID:
    b3kat_BV048542423
    Format: 1 Online-Ressource (VII, 123 p. 45 illus., 32 illus. in color)
    Edition: 1st ed. 2022
    ISBN: 9783031124020
    Additional Edition: Erscheint auch als Druck-Ausgabe ISBN 978-3-031-12401-3
    Additional Edition: Erscheint auch als Druck-Ausgabe ISBN 978-3-031-12403-7
    Language: English
    DOI: 10.1007/978-3-031-12402-0
    URL: Volltext  (URL des Erstveröffentlichers)
    Library Location Call Number Volume/Issue/Year Availability
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    Online Resource
    Online Access
    Open Access Request
    HTW Berlin
    BTU Cottbus
  • 3
    Online Resource
    Online Resource
    Interpretability for Industry 4. 0 : Statistical and Machine Learning Approaches (2022)
    Cham : Springer International Publishing AG
    Details
    UID:
    b3kat_BV049408719
    Format: 1 Online-Ressource (130 Seiten)
    Edition: 1st ed
    ISBN: 9783031124020
    Note: Description based on publisher supplied metadata and other sources , Intro -- Preface -- Contents -- 1 Different Views of Interpretability -- 1.1 Introduction -- 1.2 Interpretability: In Praise of Transparent Models -- 1.2.1 What Happened? -- 1.2.2 What Will Happen? -- 1.2.3 What Shall be Done to Make It Happen? -- 1.2.4 Patterns and Models -- 1.3 Generalizability and Interpretability with Industry 4.0 Implications -- 1.3.1 Introduction to Interpretable AI -- 1.3.2 A Wide Angle Perspective of Generalizability -- 1.3.3 Statistical Generalizability -- 1.4 Connections Between Interpretability in Machine Learning and Sensitivity Analysis of Model Outputs -- 1.4.1 Machine Learning and Uncertainty Quantification -- 1.4.2 Basics on Sensitivity Analysis and Its Main Settings -- 1.4.3 A Brief Taxonomy of Interpretability in Machine Learning -- 1.4.4 A Review of Sensitivity Analysis Powered Interpretability Methods -- References -- 2 Model Interpretability, Explainability and Trust for Manufacturing 4.0 -- 2.1 Manufacturing 4.0: Driving Trends for Data Mining -- 2.1.1 Process Monitoring in Manufacturing 4.0 -- 2.1.2 Design of Experiments in Manufacturing 4.0 -- 2.1.3 Increasing Trust in AI Models for Manufacturing 4.0: Interpretability, Explainability and Robustness -- 2.2 Additive Manufacturing as a Paradigmatic Example of Manufacturing 4.0 -- 2.3 Increase Trust in Additive Manufacturing: Robust Functional Analysis of Variance in Video-Image Analysis -- 2.3.1 The RoFANOVA Approach -- 2.3.2 An Additive Manufacturing Application -- References -- 3 Interpretability via Random Forests -- 3.1 Introduction -- 3.2 Interpretable Rule-Based Models -- 3.2.1 Literature Review -- 3.2.1.1 Definitions and Origins of Rule Models -- 3.2.1.2 Decision Trees -- 3.2.1.3 Tree-Based Rule Learning -- 3.2.1.4 Modern Rule Learning -- 3.2.2 SIRUS: Stable and Interpretable RUle Set -- 3.2.2.1 SIRUS Algorithm -- 3.2.2.2 Theoretical Analysis , 3.2.2.3 Experiments -- 3.2.3 Discussion -- 3.3 Post-Processing of Black-Box Algorithms via Variable Importance -- 3.3.1 Literature Review -- 3.3.1.1 Model-Specific Variable Importance -- 3.3.1.2 Global Sensitivity Analysis -- 3.3.1.3 Local Interpretability -- 3.3.2 Sobol-MDA -- 3.3.2.1 Sobol-MDA Algorithm -- 3.3.2.2 Sobol-MDA Properties -- 3.3.2.3 Experiments -- 3.3.3 SHAFF: SHApley eFfects Estimates via Random Forests -- 3.3.3.1 SHAFF Algorithm -- 3.3.3.2 SHAFF Consistency -- 3.3.3.3 Experiments -- 3.3.4 Discussion -- References -- 4 Interpretability in Generalized Additive Models -- 4.1 GAMs: A Basic Framework for Flexible Interpretable Regression -- 4.1.1 Flexibility Can Be Important -- 4.1.2 Making the Model Computable -- 4.1.3 Estimation and Inference -- 4.1.4 Checking, Effective Degrees of Freedom and Model Selection -- 4.1.5 GAM Computation with mgcv in R -- 4.1.6 Smooths of Several Predictors -- 4.1.7 Further Interpretable Structure -- 4.2 From GAM to GAMLSS: Interpretability for Model Building -- 4.2.1 GAMLSS Modelling of UK Aggregate Electricity Demand -- 4.2.1.1 Data Overview and Pre-processing -- 4.2.1.2 Interactive GAMLSS Model Building -- 4.3 From GAMs to Aggregations of Experts, Are We Still Interpretable? -- 4.3.1 Online Forecasting with Online Aggregation of Experts -- 4.3.2 Visualizing the Black Boxes -- References
    Additional Edition: Erscheint auch als Druck-Ausgabe Lepore, Antonio Interpretability for Industry 4. 0 : Statistical and Machine Learning Approaches Cham : Springer International Publishing AG,c2022 ISBN 9783031124013
    Language: English
    Library Location Call Number Volume/Issue/Year Availability
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    Online Resource
    HWR Berlin
  • 4
    Online Resource
    Online Resource
    Interpretability for Industry 4.0 : Statistical and Machine Learning Approaches / (2022)
    Cham :Springer International Publishing, | Cham :Springer.
    Details
    UID:
    edoccha_BV048542423
    Format: 1 Online-Ressource (VII, 123 p. 45 illus., 32 illus. in color).
    Edition: 1st ed. 2022
    ISBN: 978-3-031-12402-0
    Additional Edition: Erscheint auch als Druck-Ausgabe ISBN 978-3-031-12401-3
    Additional Edition: Erscheint auch als Druck-Ausgabe ISBN 978-3-031-12403-7
    Language: English
    DOI: 10.1007/978-3-031-12402-0
    URL: Volltext  (URL des Erstveröffentlichers)
    Library Location Call Number Volume/Issue/Year Availability
    BibTip Others were also interested in ...
    Online Resource
    Online Access
    Open Access Request
    Charité
  • 5
    Online Resource
    Online Resource
    Interpretability for Industry 4.0 : Statistical and Machine Learning Approaches / (2022)
    Cham :Springer International Publishing, | Cham :Springer.
    Details
    UID:
    edocfu_BV048542423
    Format: 1 Online-Ressource (VII, 123 p. 45 illus., 32 illus. in color).
    Edition: 1st ed. 2022
    ISBN: 978-3-031-12402-0
    Additional Edition: Erscheint auch als Druck-Ausgabe ISBN 978-3-031-12401-3
    Additional Edition: Erscheint auch als Druck-Ausgabe ISBN 978-3-031-12403-7
    Language: English
    DOI: 10.1007/978-3-031-12402-0
    URL: Volltext  (URL des Erstveröffentlichers)
    Library Location Call Number Volume/Issue/Year Availability
    BibTip Others were also interested in ...
    Online Resource
    Online Access
    Open Access Request
    FU Berlin
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