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For completeness, we also built a classifier using Geisinger data (which had 0.88 PPV, 0.74 sensitivity, and 0.99 specificity) and tested its ability to classify Stanford patients.
Secondly, we built a classifier which is a parameter-free ensemble method using only the pre-selected informative genes.
Traditional methods need to built a classifier for each flower category, and obtain large number of flower samples to train these classifiers.
We also built a classifier based on the adjective check-list scale (ACL) because this scale would be the most feasible one to be used in clinical setting.
We built a classifier to make privacy decisions on the user's behalf by detecting when context has changed and, when necessary, inferring privacy preferences based on the user's past decisions and behavior.
Project Results: The team used a mixture of novel geometric and topological methods, as well as eye-detection techniques, and built a classifier that performed very well on strong storms.
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We thus propose an efficient pruning approach to build a classifier quickly.
Support Vectors Machines (SVMs) were applied to build a classifier and reached a 98.5% success rate in the recognition of the different drugs analyzed.
Without sufficient local data, defects can be predicted via cross-project defect prediction (CPDP) utilizing data from other projects to build a classifier.
An incremental QR update scheme is used to build a classifier using a subset of these points as radial basis function centers.
The experimental results show that when supported by unlabelled samples much less labelled data is generally required to build a classifier without compromising the classification performance.
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