The classification of the same test sample was estimated 9 times, considering the optimal MLP trained and validated on different, randomly chosen, training and validation sets.
Then, models were constructed using supervised learning algorithms: C4.5, NB Tree and MLP for diagnosis of CAD and its severity The models are trained and validated using k-fold cross-validation method, where all the samples are eventually used for both training and testing.
Five variables (admission day, purpura/ecchymosis, ascites/pleural effusion, blood platelet count and pulse pressure) were further trained and validated using a ten-fold validation strategy with ten rounds of repetition [ 34, 35] using Weka 3.7.7 software [ 29].
Five variables (admission day, purpura/ecchymosis, ascites/pleural effusion, blood platelet count and pulse pressure) were finally trained and validated by a 10-fold validation strategy with 10 times of repetition, using a logistic regression model.
Each model were trained and validated by internal five-fold cross validation [Table 6].
Models were trained and validated using regularized least squares classifiers (RLSC) and fivefold cross-validation.
All these models are trained and validated by using the CD4 lymphocyte data with a 10-fold cross validation scheme.
A neural network model was designed, trained and validated.
An artificial neural network (ANN) model for stormwater temperature was trained and validated using monitoring data.
The models are trained and validated using data from traditional face-to-face and phone-based surveys.
The model is trained and validated based on the raw experimental data.
