Learning Patterns Through Artificial Contrasts With Application To
Process Control
Author(s)
E. Tuv & G. Runger
Abstract
Learning patterns through artificial contrasts
with application to process control
E. Tuvl & G. ~unger'
~nalysis & Control Technology, Intel Corporation, USA
"epartment of industrial Engineering, Arizona State University, USA
Abstract
In manufacturing as well as other application areas there is a need to learn standard
operating conditions in order to detect future changes or deviations. This is related
to the even more general problem of detecting instances (cases, records) that are
unusual compared to the bulk of the data (outliers). Examples of the problem are
fault detection in chemical engineering and statistical process control. The outlier
problem is ubiquitous.
If specific deviations are not a priori specified, this is a type of unsupervised
learning problem. The focus here is on the important, practical case for modem
data environments. That is, training data with multiple (usual many) variables of
mixed types (without the expedient assumptions common in statistics of multivariate
nor
Keywords
Related Book
Other papers in this volume
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