Research on SVM ensemble and its application to remote sensing classification

DOI

10.2991/iske.2007.102How to use a DOI?

Keywords

vector machine;Ensemble;Remote sensing classification;Fuzzy clustering

Abstract

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.

Copyright

© 2007, the Authors. Published by Atlantis Press.

Open Access

This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

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TY  - CONF
AU  - Heng-nian Qi
AU  - Mei-li Huang
PY  - 2007/10
DA  - 2007/10
TI  - Research on SVM ensemble and its application to remote sensing classification
BT  - Proceedings of the 2007 International Conference on Intelligent Systems and Knowledge Engineering (ISKE 2007)
PB  - Atlantis Press
SP  - 598
EP  - 602
SN  - 1951-6851
UR  - https://doi.org/10.2991/iske.2007.102
DO  - 10.2991/iske.2007.102
ID  - Qi2007/10
ER  -