Neural network for classification of patterns with improved method and apparatus for ordering vectors

摘要

A type of neural network called a self-organizing map (SOM) is useful in pattern classification. The ability of the SOM to map the density of the input distribution is improved with two techniques. In the first technique, the SOM is improved by monitoring the frequency for which each node is the winning node, and splitting frequently winning nodes into two nodes, while eliminating infrequently winning nodes. Topological order is preserved by inserting a link between the preceding and following nodes so that such preceding and following nodes are now adjacent in the output index space. In the second technique, the SOM is trained by applying a weight correction to each node based on the frequencies of that node and its neighbors. If any of the adjacent nodes have a frequency greater than the frequency of the present node, then the weight vector of the present node is adjusted towards the highest- frequency neighboring node. The topological order of the nodes is preserved because the weight vector is moved along a line of connection from the present node to the highest- frequency adjacent node. This second technique is suitable for mapping to an index space of any dimension, while the first technique is practical only for a one- dimensional output space.