Sentence examples for using a linear classification from inspiring English sources

Our index was combined with the neuropsychological scores using a linear classification tree.

The first one uses a linear classification algorithm to predict individual piRNAs (Zhang et al., 2011), and the second one is based on clustering approaches to predict piRNA clusters from RNAseq sequences (Jung et al., 2014; Rosenkranz and Zischler, 2012).

For k-gene signatures (k< = 4), we conducted an exhaustive search for all the k-gene combinations among the differentially expressed genes, identified from the previous step, using a linear SVM-based classification approach, and the overall accuracy was evaluated using 5-fold cross-validation.

If a support vector machine is used for a linear classification, an n-1 dimensional hyperplane is used, where n represents the number of dimensions of the data.

First, using a linear support vector machine for classification, we were able to predict individual diagnostic labels significantly more accurately (78%) from DCM-based effective connectivity estimates than from functional connectivity between (62%) or local activity within the same regions (55%).

miRNAs with high-influence on protein complexes were able to classify patients in the Taylor data [ 40] into normal vs. cancer patients with 97% classification accuracy using a linear SVM, better than prostate miRNAs (54 miRNAs) that were extracted from the literature, which gave 92% classification accuracy.

To estimate the combined predictive value of these two miRs, with and without PSA, we formed support vector machine classification models using a linear kernel.

Variables were combined, in the hope of attaining a synergistic increase in prognostic efficacy, using logistic regression analysis (LRA) modelling, a statistical technique that maximises binary classification accuracy using a linear combination of weighted input variables plus a constant term (Hosmer and Lemeshow, 1989).

Classification was performed using a linear support vector machine (SVM) algorithm [ 14].

For the sake of completeness, the classifications are repeated using a linear discriminant analysis (LDA), quadratic discriminant analysis (QDA), and k-nearest neighbor algorithm (k-NN), from which the results are compared with SVM.

Our experiments were conducted using a linear kernel, known to perform well in document classification.