Knowledge Extraction with Neural Networks: Significant Patterns and their Representation in Back-Propagation Networks

Recent turbulences of global economy impose strong requirements on efficiency, flexibility, and reliability of new products and services. Unfortunately, for most techniques applicable to the design of complex adaptive systems – artificial neural networks, fuzzy logic, cluster analysis, etc. – it is still complicated to interpret what they are actually doing – especially when processing large sets of high-dimensional data. Yet understanding and correct interpretation of the knowledge extracted by the applied model represent decisive issues for the ability to detect significant, e.g. novel input patterns, to identify their characteristic features and to assess their future development. This book provides new means to handle these problems with artificial neural networks of the Back-Propagation type. Two case studies involving image classification and analysis of economical data provided by the World Bank should help shed some light on this new and exciting area, and could be useful to professionals in the field of data mining, image processing and adaptive systems, or anyone else who may be considering applying neural networks for knowledge extraction e.g. in marketing.