We review in this paper several methods from Statistical Learning Theory (SLT) for the performance assessment and uncertainty quantification of predictive models. Computational issues are addressed so to allow the scaling to large datasets and the application of SLT to Big Data analytics. The effectiveness of the application of SLT to manufacturing systems is exemplified by targeting the derivation of a predictive model for quality forecasting of products on an assembly line.

Performance assessment and uncertainty quantification of predictive models for smart manufacturing systems

ONETO, LUCA;ORLANDI, ILENIA;ANGUITA, DAVIDE
2015-01-01

Abstract

We review in this paper several methods from Statistical Learning Theory (SLT) for the performance assessment and uncertainty quantification of predictive models. Computational issues are addressed so to allow the scaling to large datasets and the application of SLT to Big Data analytics. The effectiveness of the application of SLT to manufacturing systems is exemplified by targeting the derivation of a predictive model for quality forecasting of products on an assembly line.
2015
9781479999255
9781479999255
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11567/845898
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