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Some works have explored the vocabulary scalability issue by using more efficient classifiers.
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Furthermore, our FS algorithms allow us to build more cost-efficient classifiers which outperform baseline classifiers while only using a fraction of the features.
First, it makes training and applying a classifier more efficient by decreasing the size of the effective vocabulary.
This hybridization results in a more efficient, less complex and faster classifier for classifying satellite images.
Using these weights, Boosting algorithms can integrate weak classifiers as the strong classifier in a more efficient way and achieve excellent performance.
In comparison to ANN, using top 50 features, SVM classifier had more efficient and accurate classification results (averagely 92.71%).
The RDP-classifier was more efficient for the V4 than for the far less conserved and shorter V6 region, but differences in community structure also affected efficiency.
For routine classifier generation more efficient alternatives than a simple grid search might be required.
We suppose that the recursion process selects relevant samples for the specialized dataset from one iteration to another, leads to converge to the right target distribution, and makes the resulting classifiers more and more efficient.
The semantic similarity measure we introduced was more efficient at validating text mining results from machine learning classifiers than other measures.
The idea of AEE1 comes from the understanding that the top classifiers with higher F-scores include more efficient and important features.
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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