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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Bibliographic Details
Published inProceedings. 2005 IEEE International Geoscience and Remote Sensing Symposium, 2005. IGARSS '05 Vol. 1; p. 4 pp.
Main Authors Liu Zhigang, Shi Wenzhong, Qin Qianqing, Li Xiaowen, Xie Donghui
Format Conference Proceeding
LanguageEnglish
Published IEEE 2005
Subjects
Binary trees
Clustering algorithms
Decision trees
Extraterrestrial measurements
Kernel
Remote sensing
Support vector machine classification
Support vector machines
Training data
Tree data structures
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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.
ISBN:0780390504
9780780390508
ISSN:2153-6996
2153-7003
DOI:10.1109/IGARSS.2005.1526138