Among the top 100 features, IT features constitute between 22 and 45%, depending on the groups used to establish the categorization model.
Most existing text categorization techniques deal with monolingual documents (i.e., written in the same language) during the learning of the text categorization model and category assignment (or prediction) for unclassified documents.
Specifically, cross-lingual text categorization deals with learning a text categorization model from a set of training documents written in one language (e.g., L1) and then classifying new documents in a different language (e.g., L2).
Text categorization pertains to the automatic learning of a text categorization model from a training set of preclassified documents on the basis of their contents and the subsequent assignment of unclassified documents to appropriate categories.
They rely instead on a dubious application of the psychological principle of loss aversion and a simplistic political categorization model, among other speculative arguments, each of which is unconvincing.
And recently, this score system was revised and multiple statistically weighted clinical features were used to generate a prognostic categorization model.
That is, the expected co-occurrence statistics of the Focus and Evidence tags produced by the categorization model should be the same as the co-occurrence statistics manifested in the training data.
Firstly, as already stated, because it makes the simplistic assumption of a discrete categorization model of acetylation phenotypes (i.e., slow versus intermediate/fast acetylators) on the basis of genotypes, although heterogeneity in these categories has been recently demonstrated [ 33, 34].
The classification models selected for categorization are: Naïve Bayesian, Random Forest, and Support Vector Machine [32].
