Wavelet Time Shift Properties Integration with Support Vector Machines
<|reference_start|>Wavelet Time Shift Properties Integration with Support Vector Machines: 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.<|reference_end|>
@article{gomez2005wavelet,
    title={Wavelet Time Shift Properties Integration with Support Vector Machines},
    author={Jaime Gomez, Ignacio Melgar, Juan Seijas},
    journal={LNAI-3131 Modeling Decisions for Artificial Intelligence, ISSN
  0302-9743, p49-59, Barcelona, Spain, August-2004},
    year={2005},
    archivePrefix={arXiv},
    eprint={cs/0505053},
    primaryClass={cs.IR cs.NE}
}