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)


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.


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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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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