Summary
We propose a new scheme for enlarging generalized learning vector quantization with weighting factors for the several input dimensions which are adapted according to the specific task. This leads to a more powerful classifier with little extra cost as well as the possibility of automatically pruning irrelevant input dimensions. The method is tested on real world satellite image data and compared to several well known algorithms which determine the intrinsic data dimension.
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Hammer, B., Villmann, T. (2001). Estimating Relevant Input Dimensions for Self-organizing Algorithms. In: Advances in Self-Organising Maps. Springer, London. https://doi.org/10.1007/978-1-4471-0715-6_25
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DOI: https://doi.org/10.1007/978-1-4471-0715-6_25
Publisher Name: Springer, London
Print ISBN: 978-1-85233-511-3
Online ISBN: 978-1-4471-0715-6
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