Sentence examples similar to example based classifier from inspiring English sources

Unlike random (or passive) learning, in which a classifier randomly selects examples from which to learn, the ML based classifier actively indicates the specific examples which are commonly the most informative examples for the training task and should be labeled.

Within the same group, the MLPNN based classifier was more accurate than the LR based classifier.

Finally, DCNNs based classifier and multiclass support vector machines (SVMs) classifier are used for classification of single and complex PQDs.

The MLPNN based classifier outperformed the LR based counterpart.

The LDA based classifier showed poor internal validation (Figure S3).

This variant classifier possesses the advantages of both the traditional sparse representation based classifier and the Nearest Neighbor classifier.

Therefore, weighted sparse representation based classifier is superior to support vector machine classifier.

Overall, the accuracy of LDA based classifier achieved 94.96%.

For example, SVM or ANNs based classifiers need to be designed by training with signals corrupted by both HF noise models.

One subset is used to train a classifier after the dirty examples, based on k-nearest neighbors, are removed from the subset.

Association rule-based classifiers overcome this problem.