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The embedded methods utilize regression models with regularization.
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This new method makes it possible to utilize regression calibration in long-term studies of chronic exposure to air pollution.
Supervised methods utilize training data (input/output about the phenomenon) for classification or regression type of algorithms [31].
In particular, the proposed method utilizes penalized least squares regression to determine a piecewise constant fit to the data.
This method utilizes unlabeled instances to reduce the data distribution gap by incorporating multiple speed-up spectral regression kernel discriminant analysis (SRKDA) into the original supervised method.
This method utilizes minimal space to achieve maximum productivity.
This method utilizes functional principal component (FPC) scores to summarize the important features of functional curves of exposure levels over time and include the scores in the regression model.
This method utilizes two sensory systems.
We applied the programming algorithms for haplotype-trend regression as developed by SAS/GENETICS; these methods also utilize the SAS/STAT procedure PROC REG for the regression models[ 39, 40].
Since unsupervised methods are utilized during the classification or regression, these methods are not available to incorporate the class labels during head-pose training.
Partial least-squares (PLS) calibration and principal component regression (PCR) methods were utilized for the simultaneous spectrofluorimetric and spectrophotometric determination of pyridoxine (PY) and melatonin (MT).
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