A Review of Advances in Extreme Learning Machine Techniques and Its Applications

被引：18

作者：

Alade, Oyekale Abel ^{[1
,2
]}

Selamat, Ali ^{[1
]}

Sallehuddin, Roselina ^{[1
]}

机构：

[1] Univ Teknol Malaysia, Fac Comp, Skudai, Johor, Malaysia

[2] Fed Polytech, Comp Sci Dept, Bida, Nigeria

来源：

RECENT TRENDS IN INFORMATION AND COMMUNICATION TECHNOLOGY | 2018年 / 5卷

关键词：

Activation functions; Classification; Extreme learning machines; Single layer feedforward neural network; CLASSIFICATION; MODEL;

D O I：

10.1007/978-3-319-59427-9_91

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

Feedforward neural networks (FFNN) has been used for machine learning researches, and it really has a wide acceptance. It was noted in the recent time that feedforward neural network is far slower than required. This has created a serious bottleneck in its applications. Extreme Learning Machines (ELM) had been proposed as alternative learning algorithm to FFNN, which is characterized by single-hidden layer feedforward neural networks (SLFN). It randomly chooses hidden nodes and determines their output weight analytically. This paper review is to provide a roadmap for ELM as an efficient research tool in machine learning with the aim of finding research gap into further study. It was discovered through this study that research publications in ELM continues to grow yearly from 16.20% in 2013 to 40.83% in 2016.

引用

页码：885 / 895

页数：11

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