An Improved Extreme Learning Machine Based on Full Rank Cholesky Factorization

摘要：

　　Extreme learning machine(ELM)is a new novel learning algorithm for generalized single-hidden layer feedforward networks(SLFNs).Although it shows fast learning speed in many areas,there is still room for improvement in computational cost.To address this issue,this paper proposes an improved ELM(FRCF-ELM)which employs the full rank Cholesky factorization to compute output weights instead of traditional SVD.In addition,this paper proves in theory that the proposed FRCF-ELM has lower computational complexity.Experimental results over some benchmark applications indicate that the proposed FRCF-ELM learns faster than original ELM algorithm while preserving good generalization performance.

作者: Zuozhi Liu JinJian Wu Jianpeng Wang

作者单位: School of Mathematics & Statistics,Guizhou University of Finance and Economics,Guiyang,Guizhou,China School of Artificial Intelligence,Xidian University,Xian,Shaanxi,China Department of Mathematics and Physics,Changzhou Campus,Hohai University,Changzhou,Jiangsu,China

会议类型: 国际会议

会议名称: 2018 International Symposium on Water System Operations(ISWRSO 2018)(2018年水资源系统及调度国际研讨会)

会议地点: 北京

会议语种:英文

页码: 1-4

在线出版日期: 2018-10-12（万方平台首次上网日期，不代表论文的发表时间）