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Wavelet Time Shift Properties Integration with Support Vector Machines

  • Jaime Gómez
  • Ignacio Melgar
  • Juan Seijas
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3131)

Abstract

This paper presents a short evaluation about the integration of information derived from wavelet non-linear-time-invariant (non-LTI) projection properties using Support Vector Machines (SVM). These properties may give additional information for a classifier trying to detect known patterns hidden by noise. In the experiments we present a simple electromagnetic pulsed signal recognition scheme, where some improvement is achieved with respect to previous work. SVMs are used as a tool for information integration, exploiting some unique properties not easily found in neural networks.

Keywords

Support Vector Machine Pulse Signal Support Vector Machine Algorithm Linear Support Vector Machine Wavelet Scale 
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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References

  1. 1.
    Melgar, I., Gomez, J., Seijas, J.: Optimum Signal Linear Detector in the Discrete Wavelet Transform – Domain. In: World Scientific and Engineer Academy and Society Conference on Signal Processing, Computational Geometry, and Artificial Vision (ISCGAV 2003), Rhodes, Greece (November 2003)Google Scholar
  2. 2.
    Gomez, J., Melgar, I., Seijas, J., Andina, D.: Sub-optimum Signal Linear Detector Using Wavelets and Support Vector Machines. In: World Scientific and Engineer Academy and Society Conference on Automation and Information (ICAI 2003), Tenerife, Spain (December 2003)Google Scholar
  3. 3.
    Torres, J., Cabiscol, P., Grau, J.: Radar chirp detection through wavelet transform. In: Proc. of World Automation Congress, WAC 2002, Orlando, FL, USA (June 2002)Google Scholar
  4. 4.
    Torres, J., Vega, A., Torres, S., Andina, D.: Chirp Detection Through Discrete Wavelet Transform. In: Proceedings of the World Scientific and Engineering Academy and Society Conference on Signal Processing, Robotics And Automation (ISPRA 2002), June 2002, pp. 1971–1975 (2002)Google Scholar
  5. 5.
    Gomez, J., Melgar, I., Seijas, J.: Upgrading Pulse Detection with Time Shift Properties Using Wavelets and Support Vector Machines. In: Smirnov, M. (ed.) WAC 2004. LNCS, vol. 3457, Springer, Heidelberg (2005) (to appear)Google Scholar
  6. 6.
    Burges, C.: A Tutorial on Support Vector Machines for Pattern Recognition. Knowledge Discovery and Data Mining 2(2), 121–167 (1998)CrossRefGoogle Scholar
  7. 7.
    Mallat, S.: A theory for multiresolution signal decomposition: the wavelet representation. IEEE Trans. Pattn Anal. Mach. Intell. 11, 674–693 (1989)zbMATHCrossRefGoogle Scholar
  8. 8.
    Shannon, C.: A Mathematical Theory of Communications. Bell System Technical Journal 27, 379–423 (1948)zbMATHMathSciNetGoogle Scholar
  9. 9.
    Vapnik, V.: The Nature of Statistical Learning Theory. Springer, New York (1995)zbMATHGoogle Scholar
  10. 10.
    Burges, C.: Simplified Support Vector Decision Rules. In: Saitta, L., Schölkopf, B. (eds.) Proc. 13th International Conference on Machine Learning, San Mateo, CA, pp. 71–77. Morgan Kaufmann, San Francisco (1996)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2004

Authors and Affiliations

  • Jaime Gómez
    • 1
    • 2
  • Ignacio Melgar
    • 1
    • 3
  • Juan Seijas
    • 1
    • 3
  1. 1.Sener Ingeniería y Sistemas, S.A., Tres CantosMadridSpain
  2. 2.Escuela Politécnica SuperiorUniversidad Autónoma de Madrid 
  3. 3.Departamento de Señales, Sistema y RadiocomunicacionesUniversidad Politécnica de Madrid 

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