AN EXPERIMENTAL STUDY ON THE SELECTION OF Q-VALUE FOR THE L-GEM

摘要：

Generalization error is very important in machine learning and pattern classification. However, one can not compute the generalization error for a given problem exactly. Therefore, many research efforts have been put to estimate the generalization error for a given classification problem. Tbe Localized Generalization Error Model (L-GEM) is one of tbe recently proposed analytical generalization error upper bound models. In the L-GEM, an upper bound of generalization error of unseen samples within a Q-neighborhood of training samples is provided. The L-GEM has been widely adopted in many application areas, e.g. image classification, corporate credit risk prediction and construction productivity enhancement in civil engineering. However, the selection of Q value is vital to tbe success of L-GEM to application problems. In this work, we provide an experimental study on the selection of the Q value and found that Q value equal to half of average of input variances yield a good generalization capability of RBFNN.

关键词： Localized Generalization Error Model RBFNN L-GEM Q value

作者: JIN-CHENG LI WING W.Y.NG DANIEL S.YEUNG

作者单位: Machine Learning and Cybernetics Research Center, School of Computer Science and Engineering, South China University of Technology, Guangzhou, China

会议类型: 国际会议

会议名称: 2009 International Conference on Machine Learning and Cybernetics(2009机器学习与控制论国际会议)

会议地点: 保定

会议语种:英文

页码: 1071-1076

在线出版日期: 2009-07-12（万方平台首次上网日期，不代表论文的发表时间）