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K neighbor algorithm can not only be used for classification but also can be used for regression analysis.
Although SVM was originally developed for classification, it can also be used for regression (or function approximation).
Support vector machines (SVMs) are a type of the machines not only often used for classification and pattern recognition but can also be used for regression problems.
If there are at least a few annual data available, they can be used for regression analysis, to determine decreasing load trends caused by the slowdown.
While originally developed for classification problems, the Kohonen network can be used for regression by assigning a prediction function (such as the simple linear regression) to each class discovered by the neural network.
Support vector machine (SVM), a novel type of learning machine based on statistical learning theory, can be used for regression and time series prediction and have been reported to perform well by some promising results.
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Headache parameters were used for regression analyses.
Central cubic design was used for regression analysis.
The ratio of area responses for lovastatin was used for regression analysis.
Design Expert statistical software package version 8.05 b was used for regression analysis.
The results were used for regression and fitting process to fine an appropriate model.
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