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Besides minimizing training error, MCVELM always tries to find a separating line along which the intra-class variance is minimum, while the samples near the margin which can provide more important classification information are neglected.
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The regions of the decision echo that have high absolute values are more important for classification.
We performed a post-hoc analysis to determine which of the statistics extracted from the echoes is more informative (i.e., more important for classification) by repeatedly running the classifiers after excluding each of the statistics.
When a linear classifier is used (such as in our case) the regions of the decision echo that have high absolute (non zero) values are more important for classification.
It means these classifiers have global high categorization ability and should play a more important role in classification process instead of using the average weight simply.
Thus, only some of the more important principles of classification will be discussed.
More important, the new classification will be reviewed every year; the period of potential demotion would be so short — one vintage — that the incentive to sue would be greatly decreased.
The accuracy with consistency is more important in fault classification than the extra computation time due to the unlabeled data.
More important for the classification and understanding of the observed phase transition seems to be the view that phases differ mainly in the risk and benefit distribution.
Under this condition, the samples near the margin can provide much more important information for classification, so RELM can work better than MCVELM.
However, the errors in F3 and F4 may be increasing, because the cost function chosen places more emphasis on the first and second formants, which are believed to be more important for stress classification.
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com