Concurrent Evolution of Neural Networks and Their Data Sets

摘要：

The ultimate, goal of designing and twining a neural network is optimizing the ability to minimize the expectation of the generalization error. Because active learning techniques can be used to find optimal complexity of network, active learning has emerged as an efficient alternative to improve the generalization performance of neural networks. In this paper, we propose an evolutionary approach that can design networks automatically through active data selection, where networks and data sets are evolved at the same time. Empirical results on regression and classification show improved generalization accuracy of the proposed approach for two real-world problems.

作者: Jc-Gnn Joung Byoung-Tak Zhang

作者单位: School of Computer Sci. & Eng.Seoul National University Seoul 151-742, Korea

会议类型: 国际会议

会议名称: 8th International Conference on Neural Information Processing(ICONIP 2001)(第八届国际神经信息处理大会)

会议地点: 上海

会议语种:英文

页码: 153-158

在线出版日期: 2001-11-14（万方平台首次上网日期，不代表论文的发表时间）