Abstract

We present a new self-organizing neural network model that has two variants. The first variant performs unsupervised learning and can be used for data visualization, clustering, and vector quantization. The main advantage over existing approaches (e.g., the Kohonen feature map) is the ability of the model to automatically find a suitable network structure and size. This is achieved through a controlled growth process that also includes occasional removal of units. The second variant of the model is a supervised learning method that results from the combination of the above-mentioned self-organizing network with the radial basis function (RBF) approach. In this model it is possible—in contrast to earlier approaches—to perform the positioning of the RBF units and the supervised training of the weights in parallel. Therefore, the current classification error can be used to determine where to insert new RBF units. This leads to small networks that generalize very well. Results on the two-spirals benchmark and a vowel classification problem are presented that are better than any results previously published.

Keywords: Self-organization; Incremental learning; Radial basis function; Clustering; Data visualization; Pattern classification; Two-spiral problem; Feature map