Hierarchical support vector machines

Zhigang Liu, Wenzhong Shi, Qianqing Qin, Xiaowen Li, Donghui Xie

Research output: Chapter in book / Conference proceedingConference article published in proceeding or bookAcademic researchpeer-review

11 Citations (Scopus)

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.
Original languageEnglish
Title of host publication25th Anniversary IGARSS 2005
Subtitle of host publicationIEEE International Geoscience and Remote Sensing Symposium
Pages186-189
Number of pages4
Volume1
DOIs
Publication statusPublished - 1 Dec 2005
Event2005 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2005 - Seoul, Korea, Republic of
Duration: 25 Jul 200529 Jul 2005

Conference

Conference2005 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2005
CountryKorea, Republic of
CitySeoul
Period25/07/0529/07/05

ASJC Scopus subject areas

  • Computer Science Applications
  • Earth and Planetary Sciences(all)

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