Least Squares Twin Support Vector Machines Based on Sample Reduction for Hyperspectral Image Classification

DOI

10.2991/ameii-15.2015.223How to use a DOI?

Keywords

Least Squares Twin Support Vector Machine (LSTSVM); sample reduction; hyperspectral image.

Abstract

To overcome the low efficiency of Least Squares Twin Support Vector Machine (LSTSVM) in classifying, a new method called Sample Reduction LSTSVM (SR-LSTSVM) is proposed. The method greatly reduces the training samples and so improves the speed of LSTSVM, while the ability of LSTSVM to classify is unaffected. Our experiment results show remarkable improvement of the speed of LSTSVM on hyperspectral image, supporting our idea.

Copyright

© 2015, 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  - Li-guo Wang
AU  - Ting-ting Lu
AU  - Yue-shuang Yang
PY  - 2015/04
DA  - 2015/04
TI  - Least Squares Twin Support Vector Machines Based on Sample Reduction for Hyperspectral Image Classification
BT  - Proceedings of the International Conference on Advances in Mechanical Engineering and Industrial Informatics
PB  - Atlantis Press
SP  - 1203
EP  - 1208
SN  - 2352-5401
UR  - https://doi.org/10.2991/ameii-15.2015.223
DO  - 10.2991/ameii-15.2015.223
ID  - Wang2015/04
ER  -