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.
['Luca Oneto', 'Ilenia Orlandi', 'Davide Anguita']
Performance assessment and uncertainty quantification of predictive models for smart manufacturing systems