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1 December 2011 Morphometric Shape Analysis using Learning Vector Quantization Neural Networks — An Example Distinguishing Two Microtine Vole Species
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Abstract

Closely related species may be very difficult to distinguish morphologically, yet sometimes morphology is the only reasonable possibility for taxonomic classification. Here we present learning-vector-quantization artificial neural networks as a powerful tool to classify specimens on the basis of geometric morphometric shape measurements. As an example, we trained a neural network to distinguish between field and root voles from Procrustes transformed landmark coordinates on the dorsal side of the skull, which is so similar in these two species that the human eye cannot make this distinction. Properly trained neural networks misclassified only 3% of specimens. Therefore, we conclude that the capacity of learning vector quantization neural networks to analyse spatial coordinates is a powerful tool among the range of pattern recognition procedures that is available to employ the information content of geometric morphometrics.

© Finnish Zoological and Botanical Publishing Board 2011
Valentijn van den Brink and Folmer Bokma "Morphometric Shape Analysis using Learning Vector Quantization Neural Networks — An Example Distinguishing Two Microtine Vole Species," Annales Zoologici Fennici 48(6), (1 December 2011). https://doi.org/10.5735/086.048.0603
Received: 6 April 2011; Accepted: 1 August 2011; Published: 1 December 2011
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