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The classification techniques used to classify the PQ disturbances are based on regression algorithms.
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Some estimation algorithms, based on regression and auto-regression, for parabolic distributed parameters systems, are developed.
The fine-grained parallel mode can be expanded to other algorithms based on regression and auto-regression schemes.
Mostashari et al. [ 8] and Bork et al. [ 9] have also used EMD data based on comparable EMD coding systems but applied aberration detection algorithms based on regression analysis to control for several influencing variables (e.g. seasonality, holidays, temperature) [ 8] or dynamic forecasting models [ 9].
The algorithms are based on regression using values from adjacent and also on auto-regression using past values from the same sensor.
The algorithm is based on regression modelling that uses sufficiently small windows (5 bp default) to retain high resolution whilst correcting for one or multiple experimental control measurements simultaneously.
We estimated differential leukocyte counts for each individual using an algorithm that is based on regression calibration [ 25], and implemented in the R package minfi [ 20].
Firstly, small-scale sample subsets are constructed based on the regression tree algorithm.
This procedure is based on genetic algorithm-driven regression, using an efficient system of genetic operators' competence.
Finally, the best combination of input loads was achieved by applying evolutionary optimization techniques based on genetic algorithms (GA) to the best regression models previously obtained.
The basic classification algorithms were based on logistic regression, LS-SVMs, and kernel logistic regression.
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