The nature of statistical learning theory
The nature of statistical learning theory
An introduction to support Vector Machines: and other kernel-based learning methods
An introduction to support Vector Machines: and other kernel-based learning methods
Asymptotic behaviors of support vector machines with Gaussian kernel
Neural Computation
Estimation of High-Density Regions Using One-Class Neighbor Machines
IEEE Transactions on Pattern Analysis and Machine Intelligence
Functional Data Analysis with R and MATLAB
Functional Data Analysis with R and MATLAB
Simple linear profiles monitoring in the presence of within profile autocorrelation
Computers and Industrial Engineering
Phase II monitoring of multivariate simple linear profiles
Computers and Industrial Engineering
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In this work we focus on the use of SVMs for monitoring techniques applied to nonlinear profiles in the Statistical Process Control (SPC) framework. We develop a new methodology based on Functional Data Analysis for the construction of control limits for nonlinear profiles. In particular, we monitor the fitted curves themselves instead of monitoring the parameters of any model fitting the curves. The simplicity and effectiveness of the data analysis method has been tested against other statistical approaches using a standard data set in the process control literature.