Sentence examples for more accurate classifier from inspiring English sources
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.
The first binary chromosome represents the presence of features in the solution candidate; the second represents the confidence rates of features, which are used to assign different weights to features during the classification procedure and to achieve more accurate classifier.
Describing images with multiple features rather than a single feature, results in a more accurate classifier.
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.
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.
Therefore, a more accurate classifier will be achieved if variable weights are assigned to a kernel in different areas of the space.
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.
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).
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.
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.
