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Statistical learning methods applied to process monitoring
(23-05-2018) Apsemidis, Anastasios-Efstathios; Αψεμίδης, Αναστάσιος-Ευστάθιος; Tsiamyrtzis, Panagiotis; Karlis, Dimitrios; Athens University of Economics and Business, Department of Statistics; Psarakis, Stelios
A few years ago, Statistical Process Monitoring and Statistical Learning began to interact with each other, in order to solve difficult and complex industrial problems. In this thesis, we examine a specific statistical learning technique named Support Vector Machines.It is a most powerful algorithm, although coming from a really simple idea, which is increasingly used in many fields of statistics and computer science. We present a thorough review of the literature concerning support vector machines in the process monitoring field, we test one of the mentioned works on a real data set and try to improve its performance using Principal Components Analysis. Finally, we present an alternative approach which is able to yield better results.