Self-Organizing Maps for Pattern Classification

We are investigating novel architectures of self-organizing maps for pattern classification tasks. We started our research by investigating a recently proposed algorithm known as the neural gas (NG) algorithm. In this report, we have proposed an implicit ranking scheme to speed up the sequential implementation of the original NG algorithm. The original NG algorithm used a time consuming explicit ranking scheme. We looked at the NG algorithm rather than Kohonen's SOFM algorithm, because the NG algorithm takes a smaller number of learning steps to converge, does not require any prior knowledge about the structure of the network (i.e. topology) and its dynamics can be characterized by a global cost function. We then developed an hierarchical overlapped NG network architecture (HONG) to obtain a better classification on conflicting data. This is particularly important in totally unconstrained handwritten data, since they contain conflicting information within the same class due to various ..