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Most of the studies concentrate on proposing more elaborated features to represent documents in the vector space model, including the use of topic model techniques, such as LSI and LDA, to obtain latent semantic features.
When applying stepwise feature selection on training data to a model, techniques, such as leave-one-out, may not ensure satisfactory generalization.
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The modeling approach is complemented with other modeling techniques such as artificial neural networks (ANN) to overcome a number of modeling difficulties.
Also, special modeling techniques such as tie constraint and sub-modeling have been used to model an intermetallic layer titanium-carbide (TiC) with dimensions in nanometers, where the rest of the model's dimensions are in millimeters.
Spatial modelling techniques such as RK and OK take spatial autocorrelation into account when obtaining predictions.
Verification of compliance depends heavily upon synthetic modeling techniques such as PRA.
We also integrate a superstructure optimization with stochastic modeling techniques such as continuous-time Markov chains.
For this purpose, different modeling techniques such as Lagrangian and coupled methods are used.
The behavioral data are analyzed using different modeling techniques such as multinomial logit (MNL) and mixed logit (ML).
Thus, traditional mathematical modeling techniques, such as differential equations or difference equations, provide a limited understanding of these types of models.
In recent years, modeling techniques such as artificial neural networks have enhanced the prediction capability and the accuracy of these studies.
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com