Sentence examples for for which classification from inspiring English sources

Minimax robust classifiers tend to give too much weight to states for which classification is very difficult and therefore our effort is focused on Bayesian robust classifiers.

We think this is because agriculture was easier to detect in earlier images (cleared fields) than in late images, when it becomes spectrally similar to shrubs, grasses and fallow land (the category for which classification accuracy improved most from signature expansion), and because we had very few usable growing season images (because of clouds).

Note therefore that in MDAhet, the cluster-inference step of MDA is used to define the classes for which classification is then performed.

* missing values for 2 patients for which classification into the urban or rural group was not possible The proportion of patients who received ChEIs was 13.0%.

a) crude models b) multivariate model adjusted for all other variables * missing values for 2 patients for which classification into the urban or rural group was not possible Odds ratios from the multivariate logistic regression model predicting at least one ChEI prescription are very similar to those of the univariate analyses, with a few exceptions.

The model obtained from multivariable analysis in periampullary adenocarcinomas was tested in the ampullary subgroup in order to evaluate how well multivariable analysis with the same set of covariates could predict the prognosis in this group for which classification by histologic type of differentiation is already established.

Those patterns can be interpreted in view of the classification methods and objectives for which each classification has been developed.

Among the records for which the classification rules and the intent-flag yielded the same classification (sample A), the classifiers performed very well, with sensitivity, specificity, positive and negative predict values all above 95%.

The set of dependencies can also be used to filter cases to be classified, eliminating spurious cases, and cases for which the classification is likely to be of doubtful validity.

This would provide a general model ready to be applied in cell bursting and in any other field for which bursting classification is in progress, such as epileptic seizure modeling [15].

For every tile, within each land cover region for which unsupervised classification had taken place, examples of 'urban area' and 'rural settlement' Africover polygons were identified from the training set, where possible.