Therefore the adaption method is extended by a

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Unformatted text preview: a cluster center. Before the learning phase of the network, the two-dimensional structure of the output units is fixed and the weights are initialized randomly. During learning, the sample vectors (defining the documents) are repeatedly propagated through the network. The weights of the most similar prototype ws (winner neuron) are modified such that the prototype moves toward the input vector wi , which is defined by the currently considered document d, i.e. wi := td (competitive learning). As similarity measure usually the Euclidean distance is used. However, for text documents the scalar product (see Eq. 3) can be applied. The weights ws of the winner neuron are modified according to the following equation: w s = w s + σ · ( w s − wi ), where σ is a learning rate. To preserve the neighborhood relations, prototypes that are close to the winner neuron in the two-dimensional structure are also moved in the same direction. The weight change decreases with the distance from the winner neuron. Therefore, the adaption method is extended by a neighborhood function v (see also Fig. 3): w s = w s + v ( i , s ) · σ · ( w s − wi ), where σ is a learning rate. By this learning procedure, the...
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