"categorization model" is a correct and usable phrase in written English.
It is typically used to describe a model or process used to classify data into distinct categories. For example, "This study used a fuzzy categorization model to classify customer data."
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.
As for the categorization models, we have provided methods to retrieve the categorical information for each of the aforementioned models.
Statistical agencies and economics departments of course have their own more rigid definitions and categorization models, but the whole process seems very subjective.
DOI: http://dx.doi.org/10.7554/eLife.05457.017 We verified that the post-categorization model was best by using a leave-one-session-out cross validation.
As described above, a feature of the post-categorization model is that the psychometric curve after unilateral inactivations should be a vertical scaling of the control psychometric curve.
Nonetheless, allowing all 8 parameters to vary simultaneously, the post-categorization parameter still had the biggest change, small changes in other parameters that gave a higher likelihood than the 1-parameter post-categorization model.
