计算机工程与应用2019，Vol.55Issue(10)：73-76,4.DOI:10.3778/j.issn.1002-8331.1801-0217
大数据分割式极限学习机算法
Partitioned Extreme Learning Machine for Big Data
赵建堂1
作者信息
- 1. 咸阳师范学院 数学与信息科学学院,陕西 咸阳 712000
- 折叠
摘要
Abstract
Under the background of huge data input, a partitioned Extreme Learning Machine(ELM)algorithm is pro-posed to improve the learning speed of the ELM and reduce the memory consumption of computers. The huge data are divided into K equal parts, and weights of each ELM are trained based on each part data. The comprehensive weight of the partitioned ELM is determined based on the arithmetic average operator. To avoid the abnormal data influencing the output of ELM, Ordered Weighted Averaging(OWA)operators are used to fuse the output information of each ELM, so that the output of partitioned ELM is more stable. Numerical simulation shows that the accuracy, the learning speed and the maximum memory consumption of the partitioned ELM are higher than that of the traditional ELM, it verifies the feasi-bility and rationality of the proposed method.关键词
极限学习机(ELM)/大数据/有序加权平均算子Key words
Extreme Learning Machines(ELM)/big data/ordered weighted averaging operators分类
信息技术与安全科学引用本文复制引用
赵建堂..大数据分割式极限学习机算法[J].计算机工程与应用,2019,55(10):73-76,4.
