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| Content Provider | IEEE Xplore Digital Library |
|---|---|
| Author | Liu Zhigang Shi Wenzhong Qin Qianqing Li Xiaowen Xie Donghui |
| Copyright Year | 2005 |
| Description | Author affiliation: State Key Lab. of Remote Sensing Sci., Chinese Acad. of Sci., Beijing, China (Liu Zhigang) |
| 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. |
| File Size | 237375 |
| File Format | |
| ISBN | 0780390504 |
| DOI | 10.1109/IGARSS.2005.1526138 |
| Language | English |
| Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Publisher Date | 2005-07-29 |
| Publisher Place | South Korea |
| Access Restriction | Subscribed |
| Rights Holder | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Subject Keyword | Support vector machines Support vector machine classification Remote sensing Decision trees Kernel Tree data structures Extraterrestrial measurements Binary trees Clustering algorithms Training data |
| Content Type | Text |
| Resource Type | Article |
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