Issue No. 08 - August (2001 vol. 23)
DOI Bookmark: http://doi.ieeecomputersociety.org/10.1109/34.946991
<p><b>Abstract</b>—Biorthogonal wavelets are applied to parse multiaspect transient scattering data in the context of signal classification. A language-based genetic algorithm is used to design wavelet filters that enhance classification performance. The biorthogonal wavelets are implemented via the lifting procedure and the optimization is carried out using a classification-based cost function. Example results are presented for target classification using measured scattering data.</p>
Genetic algorithms, wavelets, classification.
E. Jones, L. Carin, L. Couchman, P. Runkle and N. Dasgupta, "Genetic Algorithm Wavelet Design for Signal Classification," in IEEE Transactions on Pattern Analysis & Machine Intelligence, vol. 23, no. , pp. 890-895, 2001.