Abstract
A method of estimation of the quality of data identification by a parametric perceptron is presented. The method allows one to combine the parametric perceptrons into a committee. It is shown by the example of the Potts perceptrons that the storage capacity of the committee grows linearly with the increase of the number of perceptrons forming the committee. The combination of perceptrons into a committee is useful when given task parameters (image dimension and chromaticity, the number of patterns, distortion level, identification reliability) one perceptron is unable to solve the identification task. The method can be applied in q-ary or binary pattern identification task.
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Kryzhanovsky, V. (2009). Pattern Identification by Committee of Potts Perceptrons. In: Alippi, C., Polycarpou, M., Panayiotou, C., Ellinas, G. (eds) Artificial Neural Networks – ICANN 2009. ICANN 2009. Lecture Notes in Computer Science, vol 5768. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-04274-4_87
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DOI: https://doi.org/10.1007/978-3-642-04274-4_87
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