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Hierarchical support vector machines

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5 Author(s)
Zhigang, L. ; State Key Lab. of Remote Sensing Sci., Chinese Acad. of Sci., Beijing, China ; Shi Wenzhong ; Qin Qianqing ; Li Xiaowen
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The speed and accuracy of a hierarchical SVM (H-SVM) depend on its tree structure. To achieve high performance, more separable classes should be separated at the upper nodes of a decision tree. Because SVM separates classes at feature space determined by the kernel function, separability in feature space should be considered. In this paper, a separability measure in feature space based on support vector data description is proposed. Based on this measure, we present two kinds of H-SVM, binary tree SVM and k-tree SVM, the decision trees of which are constructed with two bottom-up agglomerative clustering algorithms respectively. Results of experimentation with remotely sensed data validate the effectiveness of the two proposed H-SVM.

Published in:

Geoscience and Remote Sensing Symposium, 2005. IGARSS '05. Proceedings. 2005 IEEE International  (Volume:1 )

Date of Conference:

25-29 July 2005

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