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The challenge now is to try to adopt a classifier which incrementally learns the pattern of a patient's insulin intakes and predicts his blood glucose level in the near future.
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To cope with the high intra-class variability, we adopted a nonlinear classifier: the Support Vector Machines (SVM).
The basis of the mechanism described above for merging the outputs of the QUICKRBF based classifier and the RVKDE based classifier is to adopt a more cautious stand in predicting a residue to be conformationally ambivalent.
Finally, the back-propagation network model is adopted as a classifier.
Therefore, in our study, SVM was adopted as a classifier to identify the real and pseudo pre-miRNAs.
It is clear that with respect to this data set, the two consistent classifiers developed above adopt a narrower definition of BL, assigning cases with a weaker BL signal to the DLBCL category, and that a better classification result can be obtained by using a wider BL definition in the training set.
"Adopt" a tiger.
Adopt a shelter dog.
This approach adopts a linear Bayes classifier and it achieved an accuracy of 98%.
To perform classification, we adopted a diagonal covariance GMM classifier with three mixtures (one for each class of spontaneous speech).
Park et al. [ 72] adopted a similar configuration and classifier and achieved 97.5% for sensitivity and a false-positive rate of 0.27 per hour.
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