Szabo, Z; Lőrincz, A; (2007) Multilayer Kerceptron. Journal of Applied Mathematics , 24 209 - 222.

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Abstract

Multilayer Perceptrons (MLP) are formulated within Support Vector Machine (SVM) framework by constructing multilayer networks of SVMs. The coupled approximation scheme can take advantage of generalization capabilities of the SVM and the combinatory feature of the hidden layer of MLP. The network, the Multilayer Kerceptron (MLK) assumes its own backpropagation procedure that we shall derive here. Tuning rule will be provided for quadratic cost function, with regularization capability as well. A further appealing property of our approach is that by the aid of the so called kernel trick the MLK computations can be performed in the dual space.

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