Sentence examples for variable predictive model from inspiring English sources

Targeting such limitation, a new pattern recognition method – variable predictive model based class discriminate (VPMCD) is introduced into roller bearing fault identification.

Secondly, GA-VPMCD method is presented by combination genetic algorithm (GA) with conventional variable predictive model based class discriminate (VPMCD) approach.

The classifiers adopted in this paper are fuzzy neural networks (FNN), variable predictive model based class discrimination (VPMCD) method and support vector machine (SVM).

Variable predictive model class discrimination (VPMCD) is a conventional pattern recognition method; however, in practice, when the fault diagnosis method is applied to small samples or in multi-correlative feature space, the stability of the VPM constructed based on the least squares (LS) method is not sufficient.

Then SRCMFE is used to the dynamical complexity analysis of mechanical vibration signals and based on that a new fault diagnosis approach for rolling bearing is put forward by combining SRCMFE with t-distributed stochastic neighbor embedding (t-SNE) for feature dimension and the recently proposed variable predictive models based class discrimination (VPMCD) method for fault pattern recognition.

In addition, in the design of the classifier, targeting the limitation of existing pattern recognition method, a new pattern recognition method-variable predictive model based class discriminate (VPMCD) is introduced into roller bearing fault identification.

Investigating the relationship between dependent (target) and one or more independent variables (predictor) is referred to as regression analysis which is a form of predictive modeling technique [10] and can be used to compare the effects of variables measured on different scales that also evaluate the best set of variables for predictive model building.

After the application of the predictor variables, the predictive model gave us the classification rate as 90%%, which implies that the classification of a read request into correct read (1) or incorrect read category (0) was accurate to the extent of 90%% after the application of the two predictor variables.

From significant (P < 0.10), plausible and clinically relevant variables, a predictive model was generated using multivariate logistic regression.

Therefore, if the addition of these pathology clinical variables to a predictive model with variables attained solely from administrative data does not enhance model performance, their inclusion should be avoided.

One significant environmental variable in the predictive model for schistosomiasis occurrences was 'absence of asphalted street'.