Sentence examples for multivariate classifications were from inspiring English sources

Three separate multivariate classifications were carried out to (1) discriminate between which of two target positions in a single room the participant was standing ("pairwise"), (2) discriminate between all four target positions in a single room ("four-way"), and (3) discriminate between which of the two room environments the participant was in ("environment").

When multivariate classifications were conducted in which information could be integrated across the anterior and posterior temporal cortices, increases in activation in anterior regions were shown to be maximally discriminative in classifying intelligible speech, again indicating the relative importance of the left anterior STS.

The categorical explanatory variables (land use classes and pre-event landslides) for the multivariate terrain classification were computed in a GIS as the percentage of each class in each slope unit.

After 13 proteins were selected as candidate biomarkers, multivariate classification analyses were used to identify algorithms for diagnostic biomarker combinations.

Similarly, we employed multivariate logistic regression to evaluate whether other molecular signature classifications were independently predictive pCR when given HR/HER2.

Multivariate classification models were applied to recorded data in 30-minute epochs of time to predict crises between 15 and 360 minutes in the future.

Multivariate classification algorithms were used to find discriminating biomarkers and validated with another independent population of 90 breast cancer and 79 healthy controls.

For this reason, both univariate and multivariate linear classification was performed, showing that some of the univariate models are already quite satisfactory, with an improvement by multivariate analysis.

Statistically significant multivariate pattern classification was observed both within these 'feature' regions and also in the remaining V1 (responding to the rest of the face).

A multivariate classification technique was used to examine whether depressive symptoms and symptoms frequently associated with depressive disorders would cluster into recognizable syndromes that parallel traditional DSM-III psychiatric diagnoses.

Accuracy for separating PCA and tAD groups using the same multivariate classification approach was lower (72.7%) but statistically significant.