Sentence examples for multivariate classification methods from inspiring English sources

Multivariate classification methods based on machine learning techniques are available via the TMVA package.

Statistical relations between road characteristics and speed indicators were explored using multivariate classification methods and regression models.

Lastly, to our knowledge, no study in ADHD has used multivariate classification methods to task-related functional imaging data.

Class prediction was assessed to classify and to predict in which class was enclosed a sample in relation to its expression profile, using multivariate classification methods showed in Supplementary Table S4.

Confounds, systemic or otherwise, introduced in a study design cannot be recorded or tested using multivariate classification methods in as flexible a fashion as in (e.g.,) general linear models [Todd et al., 2013].

Support vector machine is a supervised, multivariate classification method [37] with optimal empirical performance in many classification settings [38] that has previously been utilized in neuroimaging research [2] [3], [7], [9].

Segmentations were generated by clustering those multi-variables using a multivariate classification method such as K-means cluster analysis [ 28].

PLS-DA is a multivariate classification method that aims to associate a dependent variable block Y (here, the ACR classification) to a covariate block X (here, the FibroDetect items).

In the context of supervised multivariate classification method as SVM [ 22], individual brain scans were treated as points located at high-dimensional space defined by the ReHo map in the preprocessed images.

Here, in addition to the traditional method of group comparisons on individual measures, we also used support vector machine (SVM) algorithms, a supervised multivariate classification method, which has proved useful, although not perfect, at distinguishing clinical groups using neuroimaging data [Ecker et al., 2010 ].

These approaches could easily spoil one of the main advantages of a multivariate classification method such as SVMs, as prefiltering of features by common univariate methods (such as the t test) might remove genes that do not reach significance when tested individually but provide useful information when taken together with other, correlated variables.