@article{ART001763288},
author={Gyeongyong Heo and Park Choong Shik and 이창우},
title={Training Sample and Feature Selection Methods for Pseudo Sample Neural Networks},
journal={Journal of The Korea Society of Computer and Information},
issn={1598-849X},
year={2013},
volume={18},
number={4},
pages={19-26}
TY - JOUR
AU - Gyeongyong Heo
AU - Park Choong Shik
AU - 이창우
TI - Training Sample and Feature Selection Methods for Pseudo Sample Neural Networks
JO - Journal of The Korea Society of Computer and Information
PY - 2013
VL - 18
IS - 4
PB - The Korean Society Of Computer And Information
SP - 19
EP - 26
SN - 1598-849X
AB - Pseudo sample neural network (PSNN) is a variant of traditional neural network using pseudo samples to mitigate the local-optima-convergence problem when the size of training samples is small. PSNN can take advantage of the smoothed solution space through the use of pseudo samples. PSNN has a focus on the quantity problem in training, whereas, methods stressing the quality of training samples is presented in this paper to improve further the performance of PSNN. It is evident that typical samples and highly correlated features help in training. In this paper,therefore, kernel density estimation is used to select typical samples and correlation factor is introduced to select features, which can improve the performance of PSNN. Debris flow data set is used to demonstrate the usefulness of the proposed methods.
KW - Pseudo sample neural network;Sample selection;Feature selection;Kernel density estimation;Correlation factor
DO -
UR -
ER -
Gyeongyong Heo, Park Choong Shik and 이창우. (2013). Training Sample and Feature Selection Methods for Pseudo Sample Neural Networks. Journal of The Korea Society of Computer and Information, 18(4), 19-26.
Gyeongyong Heo, Park Choong Shik and 이창우. 2013, "Training Sample and Feature Selection Methods for Pseudo Sample Neural Networks", Journal of The Korea Society of Computer and Information, vol.18, no.4 pp.19-26.
Gyeongyong Heo, Park Choong Shik, 이창우 "Training Sample and Feature Selection Methods for Pseudo Sample Neural Networks" Journal of The Korea Society of Computer and Information 18.4 pp.19-26 (2013) : 19.
Gyeongyong Heo, Park Choong Shik, 이창우. Training Sample and Feature Selection Methods for Pseudo Sample Neural Networks. 2013; 18(4), 19-26.
Gyeongyong Heo, Park Choong Shik and 이창우. "Training Sample and Feature Selection Methods for Pseudo Sample Neural Networks" Journal of The Korea Society of Computer and Information 18, no.4 (2013) : 19-26.
Gyeongyong Heo; Park Choong Shik; 이창우. Training Sample and Feature Selection Methods for Pseudo Sample Neural Networks. Journal of The Korea Society of Computer and Information, 18(4), 19-26.
Gyeongyong Heo; Park Choong Shik; 이창우. Training Sample and Feature Selection Methods for Pseudo Sample Neural Networks. Journal of The Korea Society of Computer and Information. 2013; 18(4) 19-26.
Gyeongyong Heo, Park Choong Shik, 이창우. Training Sample and Feature Selection Methods for Pseudo Sample Neural Networks. 2013; 18(4), 19-26.
Gyeongyong Heo, Park Choong Shik and 이창우. "Training Sample and Feature Selection Methods for Pseudo Sample Neural Networks" Journal of The Korea Society of Computer and Information 18, no.4 (2013) : 19-26.
