Detection of Tornados Using an Incremental Revised Support Vector Machine with Filters

  • Hyung-Jin Son
  • Theodore B. Trafalis
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3993)


Recently Support Vector Machines (SVMs) have played a leading role in pattern classification. SVMs are quite effective to classify static data in numerous applications. However, the use of SVMs in dynamically data driven application systems (DDDAS) is somewhat limited. This motivates the development of incremental approaches to handle DDDAS. In an incremental learning approach, it is critical to keep a certain number of support vectors (SVs) without seriously sacrificing the generalization performance of SVMs. In this paper a novel incremental SVM method, called an incremental revised support vector machine with filters (IRSVMF) is proposed to resolve the above limitations. Computational experiments with tornado data show that this approach is quite effective to reduce the number of SVs and computing time and to increase the detection rate of tornados.


Support Vector Machine Support Vector Incremental Approach Optimal Batch Size Standard SVMs 
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.
    Demeniconi, C., Gunopulos, D.: Incremental support vector machine construction. In: Proceedings of the IEEE International Conference on Data Mining, San Jose, CA, pp. 589–592 (2001)Google Scholar
  2. 2.
    Syed, N.A., Liu, H., Sung, K.K.: Incremental Learning with Support Vector Machines. In: Workshop on Support Vector Machines, International Joint Conference on Artificial Intelligence, Stockholm, Sweden (1999)Google Scholar
  3. 3.
    Shilton, A., Palaniswami, M., Ralph, D., Tsoi, A.C.: Incremental training of support vector machines. IEEE Transactions on neural networks 16(1), 114–131 (2005)CrossRefGoogle Scholar
  4. 4.
    Vapnik, V.N.: The Nature of Statistical Learning Theory. Springer, New York (1995)MATHGoogle Scholar
  5. 5.
    Haykin, S.: Neural Networks: A Comprehensive Foundation, 2nd edn. Prentice-Hall, Upper Saddle River (1998)Google Scholar
  6. 6.
    Bennet, K.P., Bredensteiner, E.J.: Duality and Geometry in SVM Classifiers. In: Proceedings of the Seventeenth International Conference on Machine Learning, pp. 57–64 (2000)Google Scholar
  7. 7.
    Crisp, D.J., Burges, C.J.C.: A Geometric Interpretation of (-SVM Classifiers. In: Advances in Neural Information Processing Systems (NIPS), vol. 12. MIT Press, Cambridge (1999)Google Scholar
  8. 8.
    Son, H., Trafalis, T.B., Richman, M.: Determination of the Optimal Batch Size in Incremental Approaches: An Application to Tornado Detection. In: Proceedings of the International Joint Conference of Neural Network, Montreal, Canada, pp. 2706–2710 (2005)Google Scholar
  9. 9.
    Cristianini, N., Shawe-Taylor, J.: An Introduction to support vector machines. Cambridge University Press, Cambridge (2000)Google Scholar
  10. 10.
    Marzban, C., Stumpf, G.J.: A Neural Network for Tornado Prediction Based on Doppler Radar Derived Attributes. Journal of Applied Meteorology 35, 617–626 (1996)CrossRefGoogle Scholar
  11. 11.
    Gunn, S.R.: Support Vector Machines for Classification and Regression. Technical Report, Image Speech and Intelligent Systems Research Group, University of Southhampton (1997)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Hyung-Jin Son
    • 1
  • Theodore B. Trafalis
    • 1
  1. 1.School of Industrial EngineeringThe University of OklahomaNormanU.S.A.

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