Sentence examples for classify examples from inspiring English sources

It is also possible to use already processed data to classify examples as yet unseen.

We will use a thematic approach to classify examples into a coherent taxonomy of problems and issues, each with a corresponding training need(s).

At least two researchers will independently classify examples within this taxonomy and through subsequent discussion with the wider team, both the taxonomy and the classification of examples within it will be refined.

Hybrid learning methods use theoretical knowledge of a domain and a set of classified examples to develop a method for accurately classifying examples not seen during training.

The aim of the weak learner is to find a good weak hypothesis h t : X → {-1, +1} for the distribution D t, where goodness is measured by the error of the hypothesis with respect to D t. Then D t is updated such that incorrectly classified examples have their weights increased, forcing the weak classifier to concentrate on the more difficult training examples.

Thus, incorrectly classified examples are more likely to be included in the next bootstrap data set.

This update procedure makes next bootstrap pick more incorrectly classified examples, i.e. difficult-to-classify examples than easy-to-classify ones.

Correspondingly, correctly classified examples are given less weight.

For multiclass discrimination LIBSVM adopts the "one-versus-one" method, in which a separate SVM is learned for each pair of classes, and majority voting among those SVM's is used when classifying examples [ 49].

Unless differences in the phenotypic characteristics were clearly described in the article, we classified examples as convergent, but point out as with the case of distinctions between parallel and convergent that these classifications may change as additional information becomes available with future research.

The expression in parentheses can only take on the values ({ 0, -1} 1}) with the zero corresponding to a correctly classified example and the non-zero values corresponding to the two different possible classification errors.