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For each subset of features, the method computes classification accuracy.
It uses weighted pseudo-loss for N samples and K classes to compute classification error.
Thus, there could be a close integration between software that computes names and software that computes classification.
In the case of resources not belonging to one of the common categories, human inspection was used as a reference to compute classification performance.
As part of this research it was decided to compute classification and misclassification rates.
For the validation sample, we computed classification accuracy based on the optimized model parameters.
The EM algorithm computes classification probabilities, not actual assignments of observations to clusters.
Beside, it provides an easy approach to evaluate the EN system performance in terms of data identification and execution time by computing the classification accuracy using a 10-fold cross validation technique.
Therefore, when computing a classification model, its performance can be tested on n different configurations of training and test data within the same dataset, while avoiding bias in the evaluation.
As a result, CLARK- l's peak RAM usage is about 3.8 GB during the index creation, and 2.8 GB when computing the classification (see Additional file 1: Table S1).
Neural networks are then used to compute the classification accuracy of the feature vectors.
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