Abstract
This paper introduces a Hybrid Multilayered Perceptron (HMLP) based classifier known as the Multi-Classify HMLP network (MCHMLP). This network is shown to be able to enhance the performance accuracy when compared to the conventional HMLP network. The Multi-Classify HMLP network architecture is trained using a Modified Recursive Prediction Error (MRPE). This study uses three benchmark datasets in order to measure the capability of the network. The results show that the proposed Multi-Classify HMLP network provides a significant improvement over the conventional HMLP network for pattern recognition applications.
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Bin Hashim, F.R., Soraghan, J.J., Petropoulakis, L. (2012). Multi-classify Hybrid Multilayered Perceptron (HMLP) Network for Pattern Recognition Applications. In: Iliadis, L., Maglogiannis, I., Papadopoulos, H. (eds) Artificial Intelligence Applications and Innovations. AIAI 2012. IFIP Advances in Information and Communication Technology, vol 381. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-33409-2_3
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DOI: https://doi.org/10.1007/978-3-642-33409-2_3
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