Research on SVM ensemble and its application to remote sensing classification
- 10.2991/iske.2007.102How to use a DOI?
- vector machine;Ensemble;Remote sensing classification;Fuzzy clustering
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.
- © 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/).
Cite this article
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 -