Sentence examples for multivariate classification techniques from inspiring English sources

Differentially-expressed genes were selected by univariate statistical tests as well as multivariate classification techniques.

The application of multivariate classification techniques on fMRI data has been shown effective in multiple studies, e.g. (LaConte et al. 2005, 2007; Sitaram et al. 2011).

Single susceptibility zonations were obtained with different multivariate classification techniques (Michie et al. 1994), including: (i) linear discriminant analysis (LDA) (Fisher 1936; Brown 1998; Venables and Ripley 2002), (ii) quadratic discriminant analysis (QDA) (Venables and Ripley 2002), and (iii) logistic regression (LR) (Cox 1958; Brown 1998; Venables and Ripley 2002).

First, alternative designs have typically utilized multivariate classification techniques [Brown et al., 2012; Dosenbach et al., 2010; Lao et al., 2004], where information about the age-related changes from multiple brain regions are combined into a model and used to predict a single estimate of age.

Among traditional classifiers, discriminant analysis is probably the most known method and can be considered the first multivariate classification technique.

We trained our prediction model using the linear discriminant analysis (LDA) technique, a widely-used multivariate classification technique for our prediction modeling.

Orthogonal projections to latent structures-discriminant analysis (OPLS-DA) [ 18, 20] is a multivariate classification technique used for finding patterns in large multivariate data sets that describe differences between the groups under study.

Applying a multivariate classification technique to labeled T1-weighted MR images of healthy adults, 56 85 years of age, Lao et al. [ 2004] accurately categorized 90% of subjects into 1 of 4 age-group brackets.

Multivariate pattern classification techniques have been applied to BCI systems across many modalities [1], [7].

Using the 2009 land use distribution and a set of morphological information, we have prepared LS zonation exploiting different multivariate statistical classification techniques.

To explore the possibility of reducing this delay, we used a multivariate pattern classification technique (linear support vector machine, SVM) to decode the true behavioral state from the measured neural signal and systematically evaluated the performance of different feature spaces (signal history, history gradient, oxygenated or deoxygenated hemoglobin signal and spatial pattern).