On the use of self organizing-maps for the representation of Barcoding data : An application to Hylomyscus data
Résumé
Self-organizing maps are used for visualizing Hylomyscus Barcoding data. The algorithm provides a two-dimensional projection of the samples and highlights the proximities and the dissimilarities between species, as well as the intra-specific variability. Since self-organizing maps are unsupervised learning, the available information on the species is only considered a posteriori. This way, it is possible to assess the hypothesis on the distribution of species and detect possible errors in the labeling of the samples.
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