Growing Self-organizing Networks - Why?

. The reasons to use growing self-organizing networks are investigated. First an overview of several models of this kind is given are they are related to other approaches. Then two examples are presented to illustrate the specific properties and advantages of incremental networks. In each case a non-incremental model is used for comparison purposes. The first example is pattern classification and compares the supervised growing neural gas model to a conventional radial basis function approach. The second example is data visualization and contrasts the growing grid model and the self-organizing feature map. 1. Introduction Growing (or incremental) network models have no pre-defined structure. Rather, they are generated by successive addition (and possibly occasional deletion) of elements. At first sight this makes them a lot more complicated than networks with static structure such as normal multi-layer-perceptrons or self-organizing maps the topology of which is chosen a priori and do..