Abstract
This paper proposes a new method of signal classification with discrete wavelet transformations and self-organizing mixture networks (SOMN) [3] being an extension of popular self-organizing maps (SOM) [1]. While SOM try to describe the sample data with a single distribution of a single parametric form, SOMN use a mixture of different distributions of different parametric forms. © 2010 Springer-Verlag.
Cite
CITATION STYLE
Lipinski, P. (2010). Signal classification with self-organizing mixture networks. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 6304 LNAI, pp. 275–276). https://doi.org/10.1007/978-3-642-15431-7_34
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