Support Vector Machines with Huffman Tree Architecture for Multiclass Classification

  • Gexiang Zhang
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3773)


This paper proposes a novel multiclass support vector machine with Huffman tree architecture to quicken decision-making speed in pattern recognition. Huffman tree is an optimal binary tree, so the introduced architecture can minimize the number of support vector machines for binary decisions. Performances of the introduced approach are compared with those of the existing 6 multiclass classification methods using U.S. Postal Service Database and an application example of radar emitter signal recognition. The 6 methods includes one-against-one, one-against-all, bottom-up binary tree, two types of binary trees and directed acyclic graph. Experimental results show that the proposed approach is superior to the 6 methods in recognition speed greatly instead of decreasing classification performance.


Support Vector Machine Binary Tree Directed Acyclic Graph Tree Architecture Digit Recognition 
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.


  1. 1.
    Vapnik, V.N.: Statistical Learning Theory. Wiley, New York (1998)zbMATHGoogle Scholar
  2. 2.
    Dibike, Y.B., Velickov, S., Solomatine, D.: Support Vector Machines: Review and Applications in Civil Engineering. In: Proceedings of the 2nd Joint Workshop on Application of AI in Civil Engineering, pp. 215–218 (2000)Google Scholar
  3. 3.
    Hsu, C.W., Lin, C.J.: A Comparison of Methods for Multiclass Support Vector Machines. IEEE Transactions on Neural Networks 13(2), 415–425 (2002)CrossRefGoogle Scholar
  4. 4.
    Cheong, S.M., Oh, S.H., Lee, S.Y.: Support Vector Machines with Binary Tree Architecture for MultiClass Classification. Neural Information Processing: Letters and Reviews 2(3), 47–51 (2004)Google Scholar
  5. 5.
    Rifkin, R., Klautau, A.: In Defence of One-Vs-All Classification. Journal of Machine Learning Research 5(1), 101–141 (2004)MathSciNetGoogle Scholar
  6. 6.
    Furnkranz, J.: Round Robin Classification, vol. 2(2), pp. 721–747 (2002)Google Scholar
  7. 7.
    Platt, J.C., Cristianini, N., Shawe-Taylor, J.: Large Margin DAG’s for Multiclass Classification. In: Advances in Neural Information Processing Systems, vol. 12, pp. 547–553. MIT Press, Cambridge (2000)Google Scholar
  8. 8.
    Guo, G.D., Li, S.Z.: Content-based Audio Classification and Retrieval by Support Vector Machines. IEEE Transactions on Neural Networks 14(1), 209–215 (2003)CrossRefGoogle Scholar
  9. 9.
    Guo, G.D., Li, S.Z., Chan, K.L.: Support Vector Machines for Face Recognition. Image and Vision Computing 19(9), 631–638 (2001)CrossRefGoogle Scholar
  10. 10.
    Huo, X.M., Chen, J.H., Wang, S.C., et al.: Support Vector Trees: Simultaneously Realizing the Principles of Maximal Margin and Maximal Purity. Technical report, 1–19 (2002), Available
  11. 11.
    Jain, A.K., Duin, R.P.W., Mao, J.C.: Statistical Pattern Recognition: a Review. IEEE Transactions on Pattern Analysis and Machine Intelligence 22(1), 4–37 (2000)CrossRefGoogle Scholar
  12. 12.
    Huang, J.H., Lai, Y.C.: Reverse Huffman Tree for Nonuniform Traffic Pattern. Electronics Letters 27(20), 1884–1886 (1991)CrossRefGoogle Scholar
  13. 13.
    Weiss, M.A.: Data Structures and Algorithm Analysis in C, 2nd edn. Addison Wesley, New York (1996)Google Scholar
  14. 14.
    Bredensteiner, E.J., Bennett, K.P.: Multicategory Classification by Support Vector Machines. Computational Optimization and Applications 12(1-3), 53–79 (1999)zbMATHCrossRefMathSciNetGoogle Scholar
  15. 15.
  16. 16.
    Frey, P.W., Slate, D.J.: Letter Recognition Using Holland-Style Adaptive Classifiers. Machine Learning 6(2), 161–182 (1991)Google Scholar
  17. 17.
    Murphy, P., Aha, D.W.: UCI Repository of Machine Learning Databases and Domain Theories (1995), Available from
  18. 18.
    Gao, D.Q., Li, R.L., Nie, G.P., et al.: Adaptive Task Decomposition and Modular Multilayer Perceptions for Letter Recognition. In: Proceedings of IEEE International Joint Conference on Neural Networks, vol. 4, pp. 2937–2942 (2004)Google Scholar
  19. 19.
    Vlad, A., Mitrea, A., Mitrea, M., et al.: Statistical Methods for Verifying the Natural Language Stationarity Based on the First Approximation. Case Study: Printed Romanian. In: Proceedings of the International Conference Venezia per il trattamento automatico dellalingue, pp. 127–132 (1999)Google Scholar
  20. 20.
    Vlad, A., Mitrea, A., Mitrea, M.: Two Frequency-Rank Laws For Letters In Printed Romanian. Procesamiento on Natural Language 24, 153–160 (2000)Google Scholar
  21. 21.
    Zhang, G.X., Hu, L.Z., Jin, W.D.: Intra-pulse Feature Analysis of Radar Emitter Signals. Journal of Infrared and Millimeter Waves 23(6), 477–480 (2004)Google Scholar
  22. 22.
    Zhang, G.X., Hu, L.Z., Jin, W.D.: Resemblance Coefficient Based Intrapulse Feature Extraction Approach for Radar Emitter Signals. Chinese Journal of Electronics 14(2), 337–341 (2005)Google Scholar
  23. 23.
    Zhang, G.X., Jin, W.D., Hu, L.Z.: A novel feature selection approach and its application. In: Zhang, J., He, J.-H., Fu, Y. (eds.) CIS 2004. LNCS, vol. 3314, pp. 665–671. Springer, Heidelberg (2004)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2005

Authors and Affiliations

  • Gexiang Zhang
    • 1
  1. 1.School of Electrical EngineeringSouthwest Jiaotong UniversityChengdu, SichuanChina

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