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A review of machine learning kernel methods in statistical process monitoring

  • Published:2020-04 Issue: Volume:142 Page:106376
  • ISSN:0360-8352
  • Container-title:Computers & Industrial Engineering
  • language:en
  • Short-container-title:Computers & Industrial Engineering

Author:

Apsemidis Anastasios,Psarakis Stelios,Moguerza Javier M.

Funder

Spanish Ministry of Science, Innovation and Universities GROMA

Statistical Methodology Lab of the Department of Statistics of the Athens University of Economics & Business.

Publisher

Elsevier BV

Subject

General Engineering,General Computer Science

Reference106 articles.

1. A combination of support vector machine and k-nearest neighbors for machine fault detection;Andre;Applied Artificial Intelligence,2013

2. An integrated method of independent component analysis and support vector machines for industry distillation process monitoring;Bo;Journal of Process Control,2010

3. Classification and regression trees;Breiman,2017

4. Robust kernel distance multivariate control chart using support vector principles;Camci;International Journal of Production Research,2008

5. Using statistical shape theory for the monitoring of nonlinear profiles;Cano;Applied Stochastic Models in Business and Industry,2015

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