Increasing Efficiency of SVM by Adaptively Penalizing Outliers

  • Yiqiang Zhan
  • Dinggang Shen
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3757)


In this paper, a novel training method is proposed to increase the classification efficiency of support vector machine (SVM). The efficiency of the SVM is determined by the number of support vectors, which is usually large for representing a highly convoluted separation hypersurface. We noted that the separation hypersurface is made unnecessarily over-convoluted around extreme outliers, which dominate the objective function of SVM. To suppress the domination from extreme outliers and thus relatively simplify the shape of separation hypersurface, we propose a method of adaptively penalizing the outliers in the objective function. Since our reformulated objective function has the similar format of the standard SVM, the idea of the existing SVM training algorithms is borrowed for training the proposed SVM. Our proposed method has been tested on the UCI machine learning repository, as well as a real clinical problem, i.e., tissue classification in prostate ultrasound images. Experimental results show that our method is able to dramatically increase the classification efficiency of the SVM, without losing its generalization ability.


Support Vector Machine Support Vector Training Sample Generalization Ability Extreme Outlier 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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  1. 1.
    Vapnik, V.N.: The Natural of Statistical Learning Theory. Springer, New York (1995)Google Scholar
  2. 2.
    Burges, C.J.C.: A Tutorial on Support Vector Machines for Pattern Recognition. Data Mining and Knowledge Discovery 2, 121–167 (1998)CrossRefGoogle Scholar
  3. 3.
    Osuna, E., Freund, R., Giosi, F.: Training Support Vector Machines: An Application to Face Detection. In: Proc. IEEE. Conf. Computer Vision and Pattern Recognition, pp. 130–136 (1997)Google Scholar
  4. 4.
    Joachims, T.: A statistical learning learning model of text classification for support vector machines. In: Proceedings of the 24th annual international ACM SIGIR conference on Research and development in information retrieval, New Orleans (2001)Google Scholar
  5. 5.
    Zhan, Y., Shen, D.: Automated Segmentation of 3D US Prostate Images Using Statistical Texture-Based Matching Method. In: Ellis, R.E., Peters, T.M. (eds.) MICCAI 2003. LNCS, vol. 2878, pp. 688–696. Springer, Heidelberg (2003)CrossRefGoogle Scholar
  6. 6.
    Wan, V., Renals, S.: Speaker Verification using Sequence Discriminant Support Vector Machines. IEEE Transactions on Speech and Audio Processing 13(2), 203–210 (2005)CrossRefGoogle Scholar
  7. 7.
    Brown, M.P.S., Grundy, W.N., Lin, D., Cristianini, N., Sugnet, C.W., Furey, T.S., Ares Jr., M., Haussler, D.: Knowledge-based analysis of microarray gene expression data by using support vector machines. Genetics 97(1), 262–267 (2000)Google Scholar
  8. 8.
    Davatzikos, C., Shen, D., Lao, Z., Xue, Z., Karacali, B.: Morphological classification of medical images using nonlinear support vector machines. In: IEEE International Symposium on Biomedical Imaging (ISBI), Arlington, VA, April 15-18 (2004)Google Scholar
  9. 9.
    Lecun, Y., Jackel, L., Bottou, L., Brunot, A., Cortes, C., Denker, J., Drunker, H., Guyon, I., Muller, U., Sackinger, E., Simard, P., Vapnik, V.: Comparison of Learning Algorithms for Handwritten Digit Recognition. In: International Conference on Artificial Neural Networks, pp. 53–60 (1995)Google Scholar
  10. 10.
    Tian, Y.-L., Brown, L., Hampapur, A., Pankanti, S., Senior, A.W., Bolle, R.M.: Real World Real-time Automatic Recognition of Facial Expressions. In: IEEE workshop on performance evaluation of tracking and surveillance, Graz, Austria, March 31 (2003)Google Scholar
  11. 11.
    Osuna, E., Girosi, F.: Reducing the run-time complexity of Support Vector Machines, ICPR, Brisbane, Australia (1998)Google Scholar
  12. 12.
    Scholkopf, B., Smola, A.J.: Learning with Kernels. The MIT Press, Cambridge (2002)Google Scholar
  13. 13.
    Burges, C.J.C.: Simplified support vector decision rules. In: Proceedings of the 13th International Conference on Machine Learning, pp. 71–77 (1996)Google Scholar
  14. 14.
    Lee, Y.-J., Mangasarian, O.L.: RSVM: Reduced support vector machines. In: Proceedings of the First SIA International Conference on Data Mining (2001)Google Scholar
  15. 15.
    Hettich, S., Blake, C.L., Merz, C.J.: Repository of machine learning databases (1998),
  16. 16.
    Manjunath, B.S., Ma, W.Y.: Texture Features for Browsing and Retrieval of Image Data. IEEE Trans. on Pattern Anal. Mach. Intell. 18, 837–842 (1996)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2005

Authors and Affiliations

  • Yiqiang Zhan
    • 1
    • 2
    • 3
  • Dinggang Shen
    • 2
    • 3
  1. 1.Dept. of Computer ScienceThe Johns Hopkins UniversityBaltimore
  2. 2.Center for Computer-Integrated Surgical Systems and TechnologyThe Johns Hopkins UniversityBaltimore
  3. 3.Section of Biomedical Image Analysis, Dept. of RadiologyUniversity of PennsylvaniaPhiladelphia

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