An efficient active set method for optimization extreme learning machines

详细信息    查看全文

• 作者：Ming-hua ZhaoAuthor Vitae ; Xiao-feng DingAuthor Vitae ; Zheng-hao ShiAuthor Vitae ; Quan-zhu YaoAuthor Vitae ; Yong-qin YuanAuthor Vitae ; Rui-yang MoAuthor Vitae
• 关键词：Optimization extreme learning machines ; Quadratic programming ; Active set ; Piecewise projected gradient
• 刊名：Neurocomputing
• 出版年：2016
• 出版时间：22 January 2016
• 年：2016
• 卷：174
• 期：part_PA
• 页码：187-193
• 全文大小：648 K

文摘

In this paper an efficient active set algorithm is presented for fast training of Optimization Extreme Learning Machines (OELMs). This algorithm suggests the use of an efficient identification technique of active set and the value reassignment technique for quadratic programming problem. With these strategies, this algorithm is able to drop many constraints from the active set at each iteration, and it can converge to the optimal solution with less iterations. The global convergence properties of the algorithm as well as its theoretical properties are analyzed. The effectiveness of the algorithm is demonstrated via benchmark datasets from many sources. Experiment results indicate that the quadratic programming problem which keeps the number of constraints in the active set as small as possible is computationally most efficient.