fivefold cross validation in one-hidden-layer and two-hidden-layer predictive neural network modelling of machining surface roughness data. J. Manuf. Syst. 24(2):93- 107, 2005.Feng, C. X. J, Yu, Z. G., and Baig, M. P., "Threefold vs Fivefold Cross validation in one-hidden layer...
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 -...
被引量: 0发表: 2011年 Specific Language Impairments in Children. Through meticulous fine-tuning of the Connectionist Temporal Classification (CTC) model on the L2-ARCTIC dataset and rigorous five-fold cross-validation, our... R Watkins,M Rice 被引量: 0发表: 1994年 加载更多0关于...
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. ...