An experimental study on stability and generalization of extreme learning machines

被引：30

作者：

Fu, Aimin ^{[1
]}

Dong, Chunru ^{[2
]}

Wang, Laisheng ^{[1
]}

机构：

[1] China Agr Univ, Coll Sci, Beijing 100083, Peoples R China

[2] Hebei Univ, Coll Math & Comp Sci, Baoding 071002, Peoples R China

来源：

INTERNATIONAL JOURNAL OF MACHINE LEARNING AND CYBERNETICS | 2015年 / 6卷 / 01期

基金：

中国国家自然科学基金;

关键词：

Extreme learning machine; Generalization capability; Uncertainty; Fuzziness; ADAPTIVE FUNCTION APPROXIMATION; STOCHASTIC CHOICE; NETWORKS;

D O I：

10.1007/s13042-014-0238-0

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

This paper gives an experimental study on the stability of an extreme learning machine (ELM) and its generalization capability. Focusing on the relationship between uncertainty of an ELM's output on the training set and the ELM's generalization capability, the experiments show some new results in the viewpoint of classical pattern recognition. The study provides some useful guidelines to choose a fraction of ELMs with better generalization from an ensemble for classification problems.

引用

页码：129 / 135

页数：7

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