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The Naïve Bayes classifier model was built using features illustrated in Table 2. Furthermore, we evaluated and selected features which were highly correlated with the classifier's predicative performance [ 23].
Thus, we developed a conditional random field (CRF) based machine learning classifier to identify section information using features illustrated in Table 2 [ 25].
This distribution is illustrated in the table below.
As illustrated in the table, the situation is no better for lawyers and business professionals.
The ratios illustrated in the table indicate strong, consistent growth.
The selection on a better propensity feature is illustrated in the feature selection session using a distance gauge.
We can see this illustrated in the truth table below.
The financial plan depends on important assumptions, most of which are illustrated in the following table.
Projected profit and loss illustrated in the following table and charts.
Considering the class operators implemented by μJava, they are developed based on object-oriented language features and Java specific features, as illustrated in Table 11.
Overall 11 subthemes (Table 3) were identified which are illustrated in the main features of the principle themes.
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