Graph-incorporated active learning with SVM

Jun Jiang; Horace H. S. Ip; Guilin Zhang

doi:10.1117/12.902931

23 November 2011 Graph-incorporated active learning with SVM

Jun Jiang, Horace H. S. Ip, Guilin Zhang

Proceedings Volume 8006, MIPPR 2011: Remote Sensing Image Processing, Geographic Information Systems, and Other Applications; 80062Q (2011) https://doi.org/10.1117/12.902931
Event: Seventh International Symposium on Multispectral Image Processing and Pattern Recognition (MIPPR2011), 2011, Guilin, China

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.

Citation Download Citation

Jun Jiang, Horace H. S. Ip, and Guilin Zhang "Graph-incorporated active learning with SVM", Proc. SPIE 8006, MIPPR 2011: Remote Sensing Image Processing, Geographic Information Systems, and Other Applications, 80062Q (23 November 2011); https://doi.org/10.1117/12.902931

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