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Improved Probabilistic Active Support Vector Machine Based Remote Sensing Image Classification

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Abstract:

Most existing methods of active support vector machine (ASVM) focus on the samples nearby the current separating hyperplane, which ignore some support vector (SV) samples which are far form the separating hyperplane, also pay not attention on whether the current separating hyperplane is close to the optimal one. In this paper a new classification method of ASVM based on improved probability-calculation method is presented. It not only presents a new method of calculating probability, but also measures the degree of closeness of the current separating hyperplane to the actual separating hyperplane by a confidence factor. Experimental results show the superiority of our proposed method both in classification accuracy and computing cost.

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Periodical:

Applied Mechanics and Materials (Volumes 303-306)

Pages:

1501-1505

DOI:

https://doi.org/10.4028/www.scientific.net/AMM.303-306.1501

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Cite this paper

Online since:

February 2013

Authors:

Chao Feng Li, Ji Wei Fan

Keywords:

Active Learning, Active Support Vector Machine (ASVM), Confidence Factor (CF), Support Vector Machine (SVM)

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© 2013 Trans Tech Publications Ltd. All Rights Reserved

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References

[1] Y. Freund, H. Seung, E. Shamir, N. Tishby, 28 (2) (1997), P. 133–168.

Google Scholar

[2] X. Zhu, P. Zhang, X. Lin, Y. Shi, IEEE Transactions on Systems, Man, and Cyber- netics, Part B: Cybernetics, Vol. 40 (2010),P. 1607–1621.

Google Scholar

[3] A. McCallum, K. Nigam, in: Proceedings of the 15th International Conference on Machine Learning (ICML), 1998, P. 350–358.

Google Scholar

[4] M. Sugiyama, S. Nakajima, Machine Learning, Vol. 75 (2009), P. 249–274.

Google Scholar

[5] Simon Tong, Daphne Koller, Journal of Machine Learning Research, Vol. 3 (2002),P. 45-66.

Google Scholar

[6] C. Campbell, N. Cristianini and A. Smola, Machine Learning, Vol. 17 (2000), P. 111-118.

Google Scholar

[7] H. Liu, T. Tu, S. Huang, Computer Science, Vol. 30 (2003), P. 110-113.

Google Scholar

[8] Klaus Brinker. Germany Proceedings of the Twentieth International Conference on Machine Learning, Washington DC , ICML-2003, (2003).

Google Scholar

[9] P. Mitra, C.A. Murthy, S.K. Pal, Pattern Recognition, Vol. 15 (2000), P. 712-715.

Google Scholar

[10] Pabitra Mitra, C.A. Murthy, S.K. Pal, IEEE Transactions on Pattern Analysis and Machine Intelligence. Vol. 3 (2004), P. 413-418.

Google Scholar

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