Sentence examples for model selection has from inspiring English sources

However, in [15, 16], the model selection has not been considered.

The Bayesian information criterion (BIC), which is a statistical criterion for model selection, has been used especially with speech material to segment and cluster a database [19].

The above indexes all reflect that the model of the internal influencing factors in the service model selection has a good fitting degree.

Although sensitivity to model selection has rarely been considered when estimating uncertainty in live-tree volume, biomass, or C stores, our analysis indicated it could be the most significant contributor to uncertainty.

It is important to understand that (1) generalization performance should be estimated once that model selection has been finalized, and (2) the patterns included in the test set should not have been involved in model selection.

Model selection has always been an important problem in statistical inference.

For nonlinear regression, methods for model selection based on a large candidate variable space include stepwise logistic regression and regression tree analysis which approximates nonlinear relationships; still for continuous variable outcomes with nonlinear models, less rigorous methods for model selection have been developed.

Bayesian model class selection has attracted substantial interest in recent years for selecting the most plausible/suitable class of models based on system input output data.

By SVC, the computation efficiency of model order selection has been highly improved.

With only one or two variables in the model, forward selection has more flexibility to identify "significant" predictors, leading to more false positive findings than backward selection.

In order to optimize the nPLS-DA models, metabolite selection has been performed using a jackknife approach.