Sentence examples for example to classify from inspiring English sources

Dr. Petraco, a chemist with a strong background in computer science, looks to industry for ideas about pattern recognition — the tools that a company like Netflix uses, for example, to classify people by the kinds of movies they like.

At the recent World Symposium on Pulmonary Hypertension (PH [Nice 2013]) there were some suggestions to update the classification of PH, for example, to classify patients with hemolytic anemia into Group 5 [ 2]; in this paper we have classified patients according to the current guidelines.

Availability: http://www2.renci.org/~jeff/software/bin/win32/ProteinVis-2.1.6-win32.zip Contact: [email protected]; [email protected] When processing large amounts of genomic or proteomic data, many analyses produce results as a tree of genes or proteins, for example, to classify genes based on results of microarray analysis, or to classify proteins based on sequence similarity.

In [51], faces were represented using elastic graphs labelled with 2-D Gabor wavelet features, and the system was trained from examples to classify faces on the basis of sex, race, and expression using linear discriminant analysis.

For example, to correctly classify all 11 true binding sites, the threshold must be chosen so low that 130 false positives are classified as binding sites of the motif.

For example, microbiologists tried to classify microorganisms based on the structures of their cell walls, their shapes, and the substances they consume.

Subjects who were occupationally exposed to dust, for example, were asked to classify their exposure as sand, cement, concrete, metal, wood, other ('please specify'), and so on.

The possibilistic formalization provides a sound encoding of non-monotonic reasoning that copes with rules with exceptions and prevents an example to be classified in more than one class.

In stead PSoL selects final classifier to classify remaining examples, our algorithm selects best classifier according to classification performance on validation set and corresponding negative set is regarded as representative negative set, final classifier is trained in the third stage.

This approach draws a random bootstrap sample of a few unlabeled examples from the set of all unlabeled examples and trains a classifier to classify the bootstrap samples as negatives along with the positive samples.

Multinomial logistic regression models were trained based on expert-curated examples and used to classify EWS-FLI1-regulated genes derived from the RNA-seq data.