A New SVM Considering the Sample Distribution

Hao Zhou; Jin Ping Sun

doi:10.4028/www.scientific.net/AMR.403-408.1302

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HomeAdvanced Materials ResearchAdvanced Materials Research Vols. 403-408A New SVM Considering the Sample Distribution

A New SVM Considering the Sample Distribution

Abstract:

As the continuation of former work, a new SVM is discussed. It is similar to the Fisher Discriminant Analysis (FDA) that the normal vector of SVM separating hyperplane is a one-dimension vector in the high-dimension hidden space, which can be used as a projection orientation to classify data. So, after the SVM training, the projection of samples can be calculated by kernel function. Finally, the threshold of the classifier is ascertained according to the distribution of projection. With the sample distribution considered, SVM performance is improved. Simulation manifests the validity of the method in this paper.

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Periodical:

Advanced Materials Research (Volumes 403-408)

Pages:

1302-1305

DOI:

https://doi.org/10.4028/www.scientific.net/AMR.403-408.1302

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Online since:

November 2011

Authors:

Hao Zhou, Jin Ping Sun

Keywords:

Classification, Classifier, Sample Distribution, Separating Hyperplane, Support Vector Machine (SVM)

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References

[1] Bian Zhaoqi, Zhang Xuegong, et al., in: Pattern Recognition. Tsinghua University Press, Beijing (2000).

Google Scholar

[2] Huang Kaizhu, Yang Haiqin, King Irwin, et al.: Maxi–Min Margin Machine: Learning Large Margin Classifiers Locally and Globally. IEEE Transactions on Neural Networks, Vol. 19(2) 2008, pp.260-272.

DOI: 10.1109/tnn.2007.905855

Google Scholar

[3] Wang Defeng, Yeung D.S., Tsang E.C.C.: Probabilistic Large Margin Machine. Proceeding of the Fifth International Conference on Machine learning and Cybernetics. Dalian, 2006, pp.2190-2195.

DOI: 10.1109/icmlc.2006.258618

Google Scholar

[4] Zhou Hao, Li Shaohong: Relative position modification of SVM's optimal hyperplane. Journal of Beijing University of Aeronautics and Astronautics. Vol. 35(11), 2009, pp.1302-1305.

Google Scholar

[5] Nello Cristianini, John Shawe-Taylor, in: An Introduction to Support Vector Machines and Other Kernel-based Learning Methods. Cambridge University Press, UK (2000).

DOI: 10.1017/cbo9780511801389

Google Scholar

[6] Li Ying, Jiao Licheng: Target recognition based on kernel Fisher discriminant. Journal of XIDIAN University. Vol. 30(2), 2003, pp.179-182.

Google Scholar

[7] http: /www. ics. uci. edu/~mlearn/MLRepository. htm.

Google Scholar