Solving XOR with a single layered perceptron by supervised self-organization of multiple output labels per class

Popular neural network learning algorithms such as Kohonen´s LVQ handle nonlinearity by assigning multiple codebook vectors per class. However, the architectural constraint requires the output units to activate in a winner-take-all fashion. In this paper, clustering of output projections developed with traditional discriminant analysis networks is achieved by allowing multiple output labels for every class: the key to such a formulation lies in the supervised self-organization algorithm which enables conventional feedforward networks to self-organize their own output labels given class information. The idea of supervised self-organization of multiple output labels has been demonstrated by implementing the XOR problem with a single layer perceptron network