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Machine learning derives its roots from statistical regression.
A statistical regression model was defined using only cutting parameters.
To quantitatively analyze the data, a statistical regression model was developed.
PLSR is a statistical regression technique similar to PLS-DA, where the independent X-variables are linearly optimized to maximally explain the variance of the response Y-variables38.
Optimized ANFIS models were compared to statistical regression models and demonstrated improved predictions in all comparisons.
The proposed methodology was measured against the benchmark of statistical regression to determine its effectiveness.
Statistical regression techniques can be used to mitigate both potential problems.
These models include empirical correlations, statistical regression and artificial neural networks (ANNs).
Compared to statistical regression models, RBFN models demonstrated an improvement of more than 20%.
Recent years have seen a surge of interest in extending statistical regression to fuzzy data.
Accuracy of this fuzzy model was compared with the accuracy of a statistical regression model.
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