Sentence examples for supervised discrimination from inspiring English sources

However, this does not necessarily mean that the supervised discrimination between the groups is also impossible.

Using a supervised Linear Discriminant Analysis (LDA), relationships between seabed parameters and acoustic classes discrimination were modelled, and acoustic classes for each data point were predicted.

Methods involving semi supervised learning exploit this gradual revealing of the truth and have been found to perform better at UXO discrimination than supervised learning methods like SVM when starting from the point dipole model [35, 36].

To ascertain data quality, we employed two supervised classification analyses: linear discrimination analysis (LDA) and K-nearest neighbor classification (KNN).

Almost all nonlinear studies so far have examined supervised approaches to patient discrimination.

In view of the significant separation achieved using PCA, supervised partial-least squares discrimination analysis (PLS-DA) (Fig.  2b) was subsequently performed to maximize the separation and identify additional metabolites to those identified using PCA.

Based on the results of the supervised clustering analysis, showing greater discrimination with the Igf2r cluster, we decided to analyse the methylation levels of the DMR of this cluster.

To demonstrate its discrimination capability, unsupervised and supervised classification models have been built and validated.

Visualization and discrimination as well as supervised and unsupervised classifications are widely employed in computational biology for the investigation and analysis of patterns hidden in wet-lab data.

We conducted supervised analyses of genes that could allow discrimination between the two classes of tumor samples (control and TF) by cross-analyzing the results given by three independent methods: supervised analysis using a signal-to-noise metric [ 18]; significance analysis of microarrays [ 19]; and Mann-Whitney test (P < 0.05).

The supervised neural network classification was performed and the discrimination ability of the proposed texture indices was found comparable with that for the sets of five GLCM texture features and four wavelet-based texture features.