计算机应用研究2016，Vol.33Issue(12)：3696-3699,4.DOI:10.3969/j.issn.1001-3695.2016.12.039
变长增量型极限学习机及其泛化性能研究
Research of variable length incremental extreme learning machine and generalization performance
摘要
Abstract
The extreme learning machine adjusted the hidden layer nodes to obtain a certain network output error and greatly raised the training speed,because it didn’t need to adjust the hidden layer node parameters in the training process.However, structure selection and over fitting limited the development of extreme learning machine.To solve the problem,this paper ana-lyzed the effect of the number of hidden layer nodes on the convergence speed and training time,and derived a new network construction mode named variable length incremental extreme learning machine (VI-ELM),its output error rate controlled the network incremental speed.The experimental results show that the proposed method can obtain good generalization performance in a more efficient way of training in regression and classification data sets.关键词
极限学习机/增量学习/泛化性能/增量型极限学习机/变长增量型极限学习机Key words
extreme learning machine (ELM)/incremental study/generalization performance/incremental extreme lear-ning machine (I-ELM)/variable length incremental extreme learning machine (VI-ELM)分类
信息技术与安全科学引用本文复制引用
王诗琦,赵书敏,耿江东,杨非,蒋忠进..变长增量型极限学习机及其泛化性能研究[J].计算机应用研究,2016,33(12):3696-3699,4.基金项目
国家自然科学基金资助项目（11303004，11573007）；航空基金资助项目（20140169001）；江苏省自然科学基金资助项目 （）
