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Each trial was assigned a class by classifying each time point in the trial using the LDA classifier.
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LDA for each of the four days was carried out on the normalized data using the lda function from the MASS package of the R language [21].
We performed linear discriminant analyses using the "lda" function in R (MASS package).
LDA was performed using the lda function from the R package "MASS".
The LDA classifier is depicted as a solid line in Figure 2. Table 3 displays some of the threshold values calculated using the designed classifier equation (y = 7.494+1.623×) for the IgG units used to classify samples as indicating primary infection (below the threshold) or secondary infection (above the threshold), as a function of sample collection day.
The SVM classifier and the LDA classifier are utilized to evaluate the performance of different fault detection techniques.
During these runs, the LDA classifier was adapted to the user after each trial.
To test the quality of the LDA classifier, leave-one-out cross-validation analysis was performed on all the response data.
We used the LDA implementation from the topic modeling toolbox [ 91].
The robustness of the LDA classifiers was tested using a bootstrap-based crossvalidation by randomly selecting half of the spectra to develop the classifiers and the remaining half to validate the classifiers [44].
We apply Genetic Algorithm (GA) to select the best feature set and this feature set is used for classification of data using Linear Discriminant Analysis (LDA) classifier.
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CEO of Professional Science Editing for Scientists @ prosciediting.com