25 March 1998 Experiments to compare rough sets and vector quantization with the self-organizing algorithm
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Abstract
This paper presents a comparison study of the rough set approach and Kohonen's vector quantization with the self- organizing algorithm. The main idea behind this research is the fact that neither the rough set nor Kohonen's neural network approaches require a prior knowledge of data distribution. The paradigms are compared in terms of their methods for the calculation of an accuracy of approximation and classification, reduction of non-significant attributes, minimal subset of attributes, and the uncertainty associated with the decision making process.
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Raisa R. Szabo, "Experiments to compare rough sets and vector quantization with the self-organizing algorithm", Proc. SPIE 3390, Applications and Science of Computational Intelligence, (25 March 1998); doi: 10.1117/12.304826; https://doi.org/10.1117/12.304826
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KEYWORDS
Neural networks

Quantization

Remote sensing

Chemical elements

Image classification

Statistical analysis

Data modeling

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