Abstract
This work describes the model of random subspace classifier and provides benchmarking results on the ELENA database. The classifier uses a coarse coding technique to transform the input real vector into the binary vector of high dimensionality. Thus, class representatives are likely to become linearly separable. Taking into account the training time, recognition time and error rate the RSC network in many cases surpasses well known classification algorithms.
An erratum to this chapter can be found at http://dx.doi.org/10.1007/11550907_163 .
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Zhora, D. (2005). Evaluating Performance of Random Subspace Classifier on ELENA Classification Database. In: Duch, W., Kacprzyk, J., Oja, E., Zadrożny, S. (eds) Artificial Neural Networks: Formal Models and Their Applications – ICANN 2005. ICANN 2005. Lecture Notes in Computer Science, vol 3697. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11550907_55
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DOI: https://doi.org/10.1007/11550907_55
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