Hierarchical support vector machines
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...
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Published in | Proceedings. 2005 IEEE International Geoscience and Remote Sensing Symposium, 2005. IGARSS '05 Vol. 1; p. 4 pp. |
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Main Authors | , , , , |
Format | Conference Proceeding |
Language | English |
Published |
IEEE
2005
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Subjects | |
Online Access | Get full text |
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Summary: | 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. |
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ISBN: | 0780390504 9780780390508 |
ISSN: | 2153-6996 2153-7003 |
DOI: | 10.1109/IGARSS.2005.1526138 |