TY - GEN
T1 - Ensemble learning with generalization performance measurement and negative correlation
AU - Tang, Yanhua
AU - Gao, Jinghuai
AU - Cui, Guangzhao
PY - 2008
Y1 - 2008
N2 - Conventional ensemble learning algorithms hased on ambiguity decomposition and negative correlation learning theory are carried out on the basis of empirical risk minimization principle. When SVM is used as the component learner, the generalization ability of ensemble learning system may not be improved. In this paper, based on the estimation of the generalization performance of SVM and negative correlation learning theory, a new selective SVM ensemble learning method is proposed. Experiments on real world data sets from UCI were carried out to demonstrate the effectiveness of this method.
AB - Conventional ensemble learning algorithms hased on ambiguity decomposition and negative correlation learning theory are carried out on the basis of empirical risk minimization principle. When SVM is used as the component learner, the generalization ability of ensemble learning system may not be improved. In this paper, based on the estimation of the generalization performance of SVM and negative correlation learning theory, a new selective SVM ensemble learning method is proposed. Experiments on real world data sets from UCI were carried out to demonstrate the effectiveness of this method.
UR - https://www.scopus.com/pages/publications/56349155625
U2 - 10.1109/IJCNN.2008.4633864
DO - 10.1109/IJCNN.2008.4633864
M3 - 会议稿件
AN - SCOPUS:56349155625
SN - 9781424418213
T3 - Proceedings of the International Joint Conference on Neural Networks
SP - 655
EP - 660
BT - 2008 International Joint Conference on Neural Networks, IJCNN 2008
T2 - 2008 International Joint Conference on Neural Networks, IJCNN 2008
Y2 - 1 June 2008 through 8 June 2008
ER -