Jack Feng C-X, Yu Z-GS, Kingi U, Pervaiz BM (2005) Threefold vs. fivefold cross validation in one-hidden-layer and two-hidden- layer predictive neural network modeling of machining surface roughness data. J Manuf Syst 24(2):93-107. doi:10.1016/S0278- 6125(05)80010-X...
FIVEFOLD CROSS-VALIDATION IN PREDICTIVENEURAL NETWORKS MODELING OF EXPERIMENTAL DATA FROMA TURNING SURFACE ROUGHNESS STUDY This study attempts to compare the performance of fivefold overthreefold CV in predictive modeling using neural networks for experimental data from a turning... ZGS Yu,CXJ Feng -...
Several cross-validation (CV) techniques are available, such as the v-fold cross-validation, leave-one-out cross-validation, and the bootstrap type of cross-validation. Among the v-fold CV methods, tenfold cross-validation is often recommended as the standard technique ...
Several cross-validation (CV) techniques are available, such as the v-fold cross-validation, leave-one-out cross-validation, and the bootstrap type of cross-validation. Among the v-fold CV methods, tenfold cross-validation is often recommended as the standard technique in predictive data mining...
Cross-validation is critical indetermining the quality of a predictive model and the costs in data collection and data mining. Several cross-validation(CV) techniques are available, such as the v-fold cross-validation, leave-one-out cross-validation, and the bootstrap typeof cross-validation. ...