In this paper Kohonen feature map is applied to the so-called two-spiral problem. Even if this network is unsupervised, the results indicate that the ability to classify or visualize the data structure depends on the training parameters. The example shows, therefore, that the network self-organization can be limited and the choices of the researcher can strongly affect the network output.
Kohonen networks and the influence of training on data structures / Morlini, Isabella. - STAMPA. - 1:(1998), pp. 370-379. (Intervento presentato al convegno 1998 IEEE Signal Processing Society Workshop tenutosi a Cambridge, UK nel 1-2 Settembre 1998).
Kohonen networks and the influence of training on data structures
MORLINI, Isabella
1998
Abstract
In this paper Kohonen feature map is applied to the so-called two-spiral problem. Even if this network is unsupervised, the results indicate that the ability to classify or visualize the data structure depends on the training parameters. The example shows, therefore, that the network self-organization can be limited and the choices of the researcher can strongly affect the network output.
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