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Pattern Recognition with the Decomposed Multilayer Perceptron

By S. Lucas, Z. Zhao, G. Cawley and P. Noakes


The Letter reports the bene ts of decomposing the multilayer perceptron (MLP) for pattern recognition tasks. Suppose there are N classes, then instead of employing 1 MLP with N outputs, N MLPs are used each with a single output. In practice, this allows fewer hidden units to be used than would be employed in the single MLP.Furthermore, it is found that decomposing the problem in this way allows convergence in fewer iterations, and it becomes straight forward to distribute the training over as many workstations as there are pattern classes. The speedup is then linear in the number of pattern classes, assuming there are as many processors as classes. If there are more classes tha

Year: 2014
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