Voting based extreme learning machine

被引：288

作者：

Cao, Jiuwen ^{[1
]}

Lin, Zhiping ^{[1
]}

Huang, Guang-Bin ^{[1
]}

Liu, Nan ^{[2
]}

机构：

[1] Nanyang Technol Univ, Sch Elect & Elect Engn, Singapore 639798, Singapore

[2] Singapore Gen Hosp, Dept Emergency Med, Singapore 169608, Singapore

来源：

INFORMATION SCIENCES | 2012年 / 185卷 / 01期

关键词：

Classification; Extreme learning machine; Majority voting; Single hidden layer feedforward networks; Ensemble methods; MULTILAYER FEEDFORWARD NETWORKS; SUPPORT VECTOR MACHINES; PATTERN-CLASSIFICATION; HIDDEN NODES; ALGORITHM; ENSEMBLE;

D O I：

10.1016/j.ins.2011.09.015

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

This paper proposes an improved learning algorithm for classification which is referred to as voting based extreme learning machine. The proposed method incorporates the voting method into the popular extreme learning machine (ELM) in classification applications. Simulations on many real world classification datasets have demonstrated that this algorithm generally outperforms the original ELM algorithm as well as several recent classification algorithms. (C) 2011 Elsevier Inc. All rights reserved.

引用

页码：66 / 77

页数：12

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