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

Автор:   Iveta M.
мягкая обложка
172 страниц
2010 год
Издательство:   Книга по Требованию
Цена:
2623 руб
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Описание: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 data sets. This book provides new means to handle these problems with neural networks of the Back- Propagation type. 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 asses their future development. Recent turbulences of global economy impose strong requirements on efficiency, flexibility, and reliability of new products and services. Two case studies involving image classification and analysis of economical data provided by the World Bank should help shed some light on this 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.


Orthomol Immun Junior Сила иммунитета на страже Вашего ребенка

2990 руб
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Учебное пособие по курсу «Нейроинформатика»

Liability for malfunction of an expert system // IEEE Conf. Manag. Expert Syst. Program and Proj., Bethesda, Md. Sept. 10-12, 1990: Proc.- Los Alamitos (Calif.) etc., 1990.- PP. 8-15. 278. Genis C. T. Relaxation and neural learning: points of convergence and divergence // J. Parallel and Distrib. Comput.- 1989.- 6, № 2.- PP. 217-244. 279. George N., Wang hen-ge, Venable D.L. Pattern recognition using the ring-wedge detector and neural-network software: [Pap.] Opt. Pattern Recogn. II: Proc. Meet., Paris, 26-27 Apr., 1989 // Proc. Soc. Photo-Opt. Instrum. Eng.- 1989.- PP. 96-106. 280. Gilev S.E. A non-back-propagation method for obtaining the gradients of estimate function // Advances in Modelling & Analysis, A, AMSE Press, 1995. Vol. 29, № 1. PP. 51-57. 281. Gilev S.E., Gorban A.N. On Completeness of the Class of Functions Computable by Neural Networks, Proc. of the World Congress on Neural Networks, Sept. 15-18, 1996, San Diego, CA, Lawrence Erlbaum Associates, 1996, pp. 984-991. 282. Gilev S.E., Gorban A.N., Kochenov D.A., Mirkes Ye.M., Golovenkin S.E., Dogadin S.A., Nozdrachev K.G., Maslennikova E.V., Matyushin G.V., Rossiev D.A., Shulman V.A., Savchenko A.A. "MultiNeuron" neural simulator and its medical applications // Proceedings of International Conference on Neural Information Processing, Oct. 17-20, 1994, Seoul, Korea.- V.2.- P.1261-1264. 283. Gilev S.E., Gorban A.N., Mirkes E.M ...»

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