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The analysis is at least initially more labour intensive than manual evaluation of samples by IC because the observer must build a classifier comprising detailed criteria for automated picking and imaging fluorescent events.
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We thus propose an efficient pruning approach to build a classifier quickly.
For the initial training phase, samples of the training set are utilized to build a classifier.
Thus, we build a classifier that extracts "first person" tweets for each of the two topics.
Use the training set L to build a classifier h1 using x1.
The authors used supervised learning techniques to build a classifier to estimate the credibility of tweets.
To build a classifier, we obtain nk training samples per class, with m features per sample.
Using a top-down approach (i.e. using differentially expressed genes to build a classifier) improved prediction accuracy to 80.9% ±2.2%
Logistic regression was used to build a classifier for prognosis.
To build a classifier, we first needed to generate a gold standard set.
The first step in self-training is to build a classifier using the labeled data.
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