Self generating neural architecture for data analysis

Abstract

Supervised and unsupervised self generating neural network architectures have been used in the recent past. Our previous work has described an unsupervised self generating feature map called the Growing Self Organizing Map (GSOM). In this paper we describe some extensions to the GSOM such that it could be used to map, and analyze, more realistic data sets.