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Intelligent Engineering Systems through Artificial Neural Networks, Volume 20

By
Cihan H. Dagli
Cihan H. Dagli
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ISBN:
9780791859599
No. of Pages:
686
Publisher:
ASME Press
Publication date:
2010

Support Vector Machines (SVMs) and other kernel methods can be applied to the monitoring of multivariate processes. Notably, kernel methods are designed to be robust to common probabilistic assumptions which is a lacking characteristic of conventional control charts. The aim of this work is to show the applications of SVMs to multivariate processes with unknown distribution and correlated characteristics. Experimental results with data sets from the UCI repository of machine learning databases showed remarkable potential.

Abstract
Introduction
Kernel Methods and Support Vector Machines
Experimental Results
Conclusions
Acknowledgments
References
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