Knowledge Extraction with Neural Networks

Iveta Mrazova

ISBN 10: 3844329412 / ISBN 13: 9783844329414

Published by LAP LAMBERT Academic Publishing Okt 2011, 2011

About this Item

This item is printed on demand - Print on Demand Titel. Neuware -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.VDM Verlag, Dudweiler Landstraße 99, 66123 Saarbrücken 176 pp. Englisch.

Seller Inventory # 9783844329414

Bibliographic Details

• Title: Knowledge Extraction with Neural Networks
• Publisher: LAP LAMBERT Academic Publishing Okt 2011
• Publication Date: 2011
• Language: English
• ISBN 10: 3844329412
• ISBN 13: 9783844329414
• Binding: Taschenbuch
• Condition: Neu
• Dimensions: 220W x 150H x 12D millimeters 220 millimeters width by 150 millimeters height by 12 millimeters depth
• Weight: 280 grams

About this title

Synopsis

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.

About the Author

Iveta Mrázová, Ph.D.: associate professor of Computer Science at Charles University in Prague. Graduated from F. Schiller University in Jena, Germany and received her Ph.D. from the Institute of Computer Science of the Czech Academy of Sciences in Prague. Was a Fulbright fellow at Missouri University of Science and Technology in Rolla, USA.