Sentence examples for classifier for discriminating from inspiring English sources
The authors adopted an error-correcting output coding (ECOG) classifier for discriminating between three states: healthy, inter-ictal, and ictal.
In order to use such a classifier for discriminating between texts with radical and non-radical content, a natural first step would be to tokenize the text.
The multivariable LASSO model based on personality, disability and physical activity is applicable despite moderate study size, however it can be considered as a weak classifier for discriminating between absence and presence of MOH.
Given the fact that the corrected AUC is 0.62 (95% CI: 0.41-0.82), our model (containing the predictors MIDAS score, MIDAS-intensity and –frequency, neuroticism score, time with moderate physical activity, educational level, hours of sleep daily and number of contacts to the headache clinic)) the model can be considered as an weak classifier for discriminating between absence and presence of MOH.
As shown in Figure 7, our method acted as a good classifier for discriminating functional binding pairs from non-functional binding pairs with AUC = 0.78.
First, a classifier for discriminating interacting compound protein pairs from the other pairs is learned based on partially known compound protein interactions.
Al-Angari et al. adopted linear kernel and second-order polynomial kernel to design the SVM classifiers for discriminating 1-mimute epochs of OSA segments from the normal segments, and to discriminate patients from the normal subjects [ 23].
Al-Angari et al. [ 23] adopted linear kernel and second-order polynomial kernel to design the SVM classifiers for discriminating 1-mimute epochs of OSA segments from the normal segments, and to discriminate patients from the normal subjects.
The signature genes identified in these studies can be used, for example, as features in computational classifiers for discriminating patients as poor or long survivors, or as responders or non-responders to treatment.
And we showed that the multivariate classifier might be effective for discriminating lung cancer patients.
Our study suggests that the MLP classifier can be implemented for discriminating LC and non-LC cohorts by using machine learning method based on the routine available clinical parameters.
