[KSCI] Korea Science Citation Index Service

http://dx.doi.org/10.5351/CKSS.2009.16.5.881

Visualizing SVM Classification in Reduced Dimensions

Huh, Myung-Hoe (Department of Statistics, Korea University)
Park, Hee-Man (Department of Statistics, Korea University)

Publication Information

Communications for Statistical Applications and Methods / v.16, no.5, 2009 , pp. 881-889 More about this Journal

Abstract

Support vector machines(SVMs) are known as flexible and efficient classifier of multivariate observations, producing a hyperplane or hyperdimensional curved surface in multidimensional feature space that best separates training samples by known groups. As various methodological extensions are made for SVM classifiers in recent years, it becomes more difficult to understand the constructed model intuitively. The aim of this paper is to visualize various SVM classifications tuned by several parameters in reduced dimensions, so that data analysts secure the tangible image of the products that the machine made.

Keywords

Support vector machine(SVM); dimensional reduction; model visualization;

Citations & Related Records

Times Cited By KSCI : 2 (Citation Analysis)

Reference
Cited By KSCI

1	Platt, J. (2000). Probabilistic outputs for support vector machines and comparison to regularized likelihood methods. In Advances in Large Margin Classifiers, Edited by A. Smola, P. Bartlett, B. Scholkopf and D. Dchuurmans. Cambridge, MA
2	Vapnik, V. N. (1999). The Nature of Statistical Learning Theory, Springer, New York
3	Wang, J., Shen, X. and Liu, Y. (2008). Probability estimation for large-margin classifiers, Biometrika, 95, 149-167 DOI ScienceOn
4	Wickham, H., Caragea, D. and Cook, D. (2006). Exploring high-dimensional classification boundaries, Unpublished manuscript
5	Wu, T. F., Lin, C. J. and Weng, R. C. (2004). Probability estimates for multi-class classification by pairwise coupling, Journal of Machine Learning Research, 5, 975-1005
6	Huh, M. H. (2009). Visualizing multi-variable prediction functions by segmented k-CPG's, Commu-nications of the Korean Statistical Society, 16, 185-193 과학기술학회마을 DOI ScienceOn
7	Huh, M. H. and Lee, Y. G. (2008). Simple graphs for complex prediction functions, Communications of the Korean Statistical Society, 15, 343-351 과학기술학회마을 DOI ScienceOn
8	Hastie, T., Tibshirani, R. and Friedman, J. (2001). The Elements of Statistical Learning: Data Mining, Inference, and Prediction, Springer, New York
9	Chang, C. C. and Lin, C. J. (2001). LIBSVM : a library for support vector machines. available at http://www.csie.ntu.edu.tw/~cjin/libsvm
10	Cook, D. and Swayne, D. (2007). Interactive and Dynamic Graphics for Data Analysis, Springer, New York