Sentence examples for using the linear discriminant from inspiring English sources
We trained our prediction model using the linear discriminant analysis (LDA) technique, a widely-used multivariate classification technique for our prediction modeling.
In the second step, we develop the corresponding Quantitative Proteome Property Relationship (QPPR) models using the Linear Discriminant Analysis (LDA).
The features are extracted from a single high-resolution gray-scale image of the palmar surface of the hand using the linear discriminant analysis (LDA) appearance-based feature-extraction approach.
Using the linear discriminant analysis (LDA) effect size (LEfSe) algorithm, relative abundances of Ruminococcaceae, norank_f_ Ruminococcaceae, Ruminococcaceae-NK4A214_group, Veillonella and Prevotellaceae decreased when pigs were fed the WB diet.
Using the linear discriminant analysis (LDA) effect size (LEfSe) method (Segata et al., 2011), 67 KEGG database biochemical pathways (P < 0.05) were identified as significantly responding to the dietary intervention (Fig. 4c).
Using PCA, we extracted the top five uncorrelated features from each of the five regions covered by the EEG scalp electrodes using the linear discriminant backward search technique (because it produced the highest AUC value of 91%).
In our experiment, we use the linear discriminant analysis (LDA) approach and locality preserving projection (LPP) approach for intersession compensation.
For this reason, we can use the linear discriminant analysis and piecewise models in combination to classify and predict the stability of the mutant Arc homodimers.
For Ward and Pim (1984) and Ruiz-Navarro et al. (1998), we used the linear discriminant function method to estimate relative risks from comparisons of means (Greenland 1987).
We then used the "predict" function to classify individual flies based on resubstitution using the linear discriminate scores for all three discriminant factors, and cross-validated the classification using the "CV" command in the "lda" function.
The experimental results demonstrated higher test recognition rates of Gaussian OAA SVMs on random unknown ECG data sets with the use of the Kernel Principal Component Analysis (KPCA) as compared to the use of the Linear Discriminant Analysis (LDA) and Principal Component Analysis (PCA).
