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Exact(12)
We observe that the proposed posterior smoothing in the acoustic space can further reduce classification errors.
As a result, LB, which is built on these classifiers, can significantly reduce classification error, compared with the traditional bagging (TB) approach.
In particular, by taking full advantage of xk's nearest neighbors, classifiers are able to reduce classification bias and variance when classifying xk.
This demonstrates that incorporating 2s of past data points can reduce classification delay.
This method is commonly used to reduce classification bias and estimate future model performance [40].
Some of them are biologically meaningful in explaining gene expression, and thus have the potential to further reduce classification errors.
Similar(48)
The usage of the reduced set of features and SVM classifier gave only slightly reduced classification performances, which did not differ from the full sets of features.
Feature dimension reduction using principal component analysis (PCA) may provide effective classification performance and reduced classification time.
In the present literature, there exists no other distinct way to optimally select the features without reducing classification performance.
Empirical results confirm that LB can statistically significantly outperform alternative methods in terms of reducing classification error.
On the other hand, having too many regions reduces classification efficiency and may also lower recognition accuracy due to too much unnecessary location information being included.
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Since I tried Ludwig back in 2017, I have been constantly using it in both editing and translation. Ever since, I suggest it to my translators at ProSciEditing.

Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com