Research on SVM ensemble and its application to remote sensing classification

被引：3

作者：

Qi, Heng-nian ^{[1
]}

Huang, Mei-li ^{[1
]}

机构：

[1] Zhejang Forestry Univ, Sch Informat Engn, Linan 311300, Peoples R China

来源：

PROCEEDINGS OF THE INTERNATIONAL CONFERENCE ON INTELLIGENT SYSTEMS AND KNOWLEDGE ENGINEERING (ISKE 2007) | 2007年

关键词：

vector machine; ensemble; remote sensing classification; fuzzy clustering;

D O I：

10.2991/iske.2007.102

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

The paper analyzes the key concepts, theories and methods of machine learning ensemble, and reviews the related studies on support vector machine (SVM) ensemble. The experiments on the remote sensing classification show that SVM ensemble is more accurate than single SVM. To obtain an effective SVM ensemble, we propose a selective SVM ensemble approach based on fuzzy clustering and discuss the issues on it.

引用

页数：1

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