Abstract
This paper investigates the possibility of improving the classification capability of single-layer and multilayer perceptrons by incorporating additional output layers. This Multi-Output-Layer Perceptron (MOLP) is a new type of constructive network, though the emphasis is on improving pattern separability rather than network efficiency. The MOLP is trained using the standard back-propagation (BP) algorithm. The studies are concentrated on realizations of arbitrary functions which map from an x-dimensional input MOLP, all problems existing in an original n-dimensional space in the hidden layer are transformed to a higher (n +1)-dimensional space, so that the possibility of linear separability is increased. Experimental investigations show that the classification ability of the MOLP is superior to that of an equivalent MLP. In general, this performance increase can be achieved with shorter training times and simpler network architectures.
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Owens, F.J., Zheng, G.H. & Irvine, D.A. A Multi-Output-Layer Perceptron. Neural Comput & Applic 4, 10–20 (1996). https://doi.org/10.1007/BF01413865
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DOI: https://doi.org/10.1007/BF01413865