Constrained Extreme Learning Machine: a Novel Highly Discriminative Random Feedforward Neural Network

被引：0
|
作者：
Zhu, Wentao [1 ]
Miao, Jun [1 ]
Qing, Laiyun [2 ]
机构：
[1] Chinese Acad Sci, Key Lab Intelligent Informat Proc, Inst Comp Technol, Beijing 100190, Peoples R China
[2] Univ Chinese Acad Sci, Sch Comp & Control Engn, Beijing 100049, Peoples R China
来源：
PROCEEDINGS OF THE 2014 INTERNATIONAL JOINT CONFERENCE ON NEURAL NETWORKS (IJCNN) | 2014年
关键词：
CLASSIFICATION;
D O I：
暂无
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
In this paper, a novel single hidden layer feedforward neural network, called Constrained Extreme Learning Machine (CELM), is proposed based on Extreme Learning Machine (ELM). In CELM, the connection weights between the input layer and hidden neurons are randomly drawn from a constrained set of difference vectors of between-class samples, rather than an open set of arbitrary vectors. Therefore, the CELM is expected to be more suitable for discriminative tasks, whilst retaining other advantages of ELM. The experimental results are presented to show the high efficiency of the CELM, compared with ELM and some other related learning machines.
引用
收藏
页码：800 / 807
页数：8
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