Ant Colonies (or Swarm Intelligence) and Self-Organizing Maps (a type of unsupervised Neural Network) have become two important and powerful classification heuristics in computer science and artificial intelligence. After first describing each model, a hybrid is introduced that has the visual appeal of swarm intelligence and the efficiency of self-organizing maps.

Self-organizing feature maps with self-determined local neighborhood widths are applied to construct principal manifolds of data distributions. This task exempli es the problem of the learning of learning parameters in neural networks. The proposed algorithm is based upon analytical results on phase transitions in self-organizing feature maps available for idealized situations. By illustrative ...

Today's information age may be characterized by constant massive production and dissemination of written information. More powerful tools for exploring, searching, and organizing the available mass of information are needed to cope with this situation. In this context the map metaphor for displaying the contents of a document archive in a two-dimensional display has gained increased interest. I...

The self-organizing map’s unsupervised clustering property, is known for classifying high dimensional data sets into clusters that have similar features. Using this property and arranging self-organizing maps into hierarchies, we demonstrate in this paper that legacy code can be potentially broken down into suggested classes using hierarchical self-organizing maps. This is in conjunction with i...

Multiscale structures and algorithms that unify the treatment of local and global scene information are of particular importance in image segmentation. Vector quantization, owing to its versatility, has proved to be an effective means of image segmentation. Although vector quantization can be achieved using self-organizing maps with competitive learning, self-organizing maps in their original s...

Digital document libraries are an almost perfect application arena for un-supervised neural networks. This because many of the operations computers have to perform on text documents are classiication tasks based on \noisy" input patterns. The \noise" arises because of the known inaccuracy of mapping natural language to an indexing vocabulary representing the contents of the documents. A growing...