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Proceedings Paper

Graph-incorporated active learning with SVM
Author(s): Jun Jiang; Horace H. S. Ip; Guilin Zhang
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Paper Abstract

Active learning is typically limited by the small sample problem which makes the resulting classifiers perform poorly, especially in the initial stages. To overcome this problem, in this paper, a novel framework - graph-incorporated active learning - is proposed, in which the selection pool is regarded as a graph. Its graph structure is applied to both improve sample selection criterion and provide the learner enough pseudo-labeled samples. By comparing with the state-of-theart technique, the experiments on benchmark datasets show that the improvement of the proposed method is significant, i.e., it can solve the small problem well. The framework is combined with, but is not limited to, SVM.

Paper Details

Date Published: 23 November 2011
PDF: 8 pages
Proc. SPIE 8006, MIPPR 2011: Remote Sensing Image Processing, Geographic Information Systems, and Other Applications, 80062Q (23 November 2011); doi: 10.1117/12.902931
Show Author Affiliations
Jun Jiang, Huazhong Univ. of Science and Technology (China)
Horace H. S. Ip, City Univ. of Hong Kong (Hong Kong, China)
Guilin Zhang, Huazhong Univ. of Science and Technology (China)


Published in SPIE Proceedings Vol. 8006:
MIPPR 2011: Remote Sensing Image Processing, Geographic Information Systems, and Other Applications
Faxiong Zhang; Faxiong Zhang, Editor(s)

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