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Exact(6)
We have thus proposed a greedy algorithm for estimating optimal subsets and demonstrated that the resulting classifier can produce more accurate classifiers in both simulated and real applications.
Subsequently, the variables required by the more accurate classifiers are measured (by administrating a subset of the tests), and the group the child should belong to is reassessed.
Classifier accuracy is loosely correlated with the size of the positive set, where TFs with more known targets tend to have more accurate classifiers (Additional File 3).
We also plan to apply an ensemble approach for integrating these two frameworks because more accurate classifiers are not only obtained by combining different data types but also by combining individual decisions of multiple classifiers.
This allows us to build more accurate classifiers while at the same time bridging the gap between the black box behavior and the end-user who has to interpret the results.
Implementation of our quality control tool alleviated this effect, however it is noteworthy that even more accurate classifiers can be identified for analyses that focus on a given cell type or a given time point, extending the range of potential applications of our pipeline.
Similar(54)
Describing images with multiple features rather than a single feature, results in a more accurate classifier.
The larger the loss, the larger the weight decrease occurs to such classifier causing the more accurate classifier to have a higher weight for prediction purposes.
Using multiple features instead of a single one results in a more accurate classifier in an image classification task as discussed in section 1.1.
Therefore, a more accurate classifier will be achieved if variable weights are assigned to a kernel in different areas of the space.
In many domains when we have several competing classifiers available we want to synthesize them or some of them to get a more accurate classifier by a combination function.
Related(20)
more correct classifiers
more independent classifiers
more particular classifiers
more complex classifiers
more parsimonious classifiers
more efficient classifiers
more powerful classifiers
more individual classifiers
more reproducible classifiers
more informative classifiers
more robust classifiers
more advanced classifiers
more accurate predictions
more sophisticated classifiers
more complicated classifiers
more suitable classifiers
more accurate instruments
more accurate drawings
more effective classifiers
more accurate results
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