Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/30701
Title: Hierarchical support vector machines
Authors: Liu, Z
Shi, W 
Qin, Q
Li, X
Xie, D
Issue Date: 2005
Source: International Geoscience and Remote Sensing Symposium (IGARSS), 2005, v. 1, 1526138, p. 186-189 How to cite?
Abstract: 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.
Description: 2005 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2005, Seoul, 25-29 July 2005
URI: http://hdl.handle.net/10397/30701
ISBN: 0780390504
9780780390508
DOI: 10.1109/IGARSS.2005.1526138
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