Unsupervised Learning in Artificial Neural Networks
Darryl Charles (University of Ulster, Ireland), Colin Fyfe (University of Paisley, UK), Daniel Livingstone (University of Paisley, UK) and Stephen McGlinchey (University of Paisley, UK)
Copyright: © 2008
With the artificial neural networks which we have met so far, we must have a training set on which we already have the answers to the questions which we are going to pose to the network. Yet humans appear to be able to learn (indeed some would say can only learn) without explicit supervision. The aim of unsupervised learning is to mimic this aspect of human capabilities and hence this type of learning tends to use more biologically plausible methods than those using the error descent methods of the last two chapters. The network must self-organise and to do so, it must react to some aspect of the input data - typically either redundancy in the input data or clusters in the data; i.e. there must be some structure in the data to which it can respond.