This book is proposing a hybrid algorithm of two fuzzy genetic-based machine learning approaches - Michigan and Pittsburgh - for designing fuzzy rule-based classification systems. The search ability of each approach is examined to efficiently find fuzzy rule-based systems with high classification accuracy. These two approaches are combined into a single hybrid algorithm. The generalization ability of fuzzy rule-based classification systems, designed by the proposed hybrid algorithm is examined on real data sets. Experimental results show that the hybrid algorithm has higher search ability within a population of individual rules and within a population of rule sets.
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Vendeur : BuchWeltWeit Ludwig Meier e.K., Bergisch Gladbach, Allemagne
Taschenbuch. Etat : Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -This book is proposing a hybrid algorithm of two fuzzy genetic-based machine learning approaches - Michigan and Pittsburgh - for designing fuzzy rule-based classification systems. The search ability of each approach is examined to efficiently find fuzzy rule-based systems with high classification accuracy. These two approaches are combined into a single hybrid algorithm. The generalization ability of fuzzy rule-based classification systems, designed by the proposed hybrid algorithm is examined on real data sets. Experimental results show that the hybrid algorithm has higher search ability within a population of individual rules and within a population of rule sets. 140 pp. Englisch. N° de réf. du vendeur 9783659891038
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Vendeur : moluna, Greven, Allemagne
Etat : New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Autor/Autorin: Ahmed LamiaaDr. Lamiaa H. Ahmed is a Lecturer of Computer Science at Modern Academy in Maadi, Cairo, Egypt. Computational Intelligence, Evolutionary Programming, Membrane Computing, Fuzzy Logic, Organic Computing and Java programming. N° de réf. du vendeur 158248679
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Vendeur : Revaluation Books, Exeter, Royaume-Uni
Paperback. Etat : Brand New. 140 pages. 8.66x5.91x0.32 inches. In Stock. N° de réf. du vendeur 3659891037
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Vendeur : buchversandmimpf2000, Emtmannsberg, BAYE, Allemagne
Taschenbuch. Etat : Neu. This item is printed on demand - Print on Demand Titel. Neuware -This book is proposing a hybrid algorithm of two fuzzy genetic-based machine learning approaches - Michigan and Pittsburgh - for designing fuzzy rule-based classification systems. The search ability of each approach is examined to efficiently find fuzzy rule-based systems with high classification accuracy. These two approaches are combined into a single hybrid algorithm. The generalization ability of fuzzy rule-based classification systems, designed by the proposed hybrid algorithm is examined on real data sets. Experimental results show that the hybrid algorithm has higher search ability within a population of individual rules and within a population of rule sets.Books on Demand GmbH, Überseering 33, 22297 Hamburg 140 pp. Englisch. N° de réf. du vendeur 9783659891038
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Vendeur : preigu, Osnabrück, Allemagne
Taschenbuch. Etat : Neu. Evolutionary Machine Learning in Linguistic Knowledge Extraction | Lamiaa Ahmed (u. a.) | Taschenbuch | 140 S. | Englisch | 2016 | LAP LAMBERT Academic Publishing | EAN 9783659891038 | Verantwortliche Person für die EU: BoD - Books on Demand, In de Tarpen 42, 22848 Norderstedt, info[at]bod[dot]de | Anbieter: preigu Print on Demand. N° de réf. du vendeur 103715507
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Vendeur : AHA-BUCH GmbH, Einbeck, Allemagne
Taschenbuch. Etat : Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - This book is proposing a hybrid algorithm of two fuzzy genetic-based machine learning approaches - Michigan and Pittsburgh - for designing fuzzy rule-based classification systems. The search ability of each approach is examined to efficiently find fuzzy rule-based systems with high classification accuracy. These two approaches are combined into a single hybrid algorithm. The generalization ability of fuzzy rule-based classification systems, designed by the proposed hybrid algorithm is examined on real data sets. Experimental results show that the hybrid algorithm has higher search ability within a population of individual rules and within a population of rule sets. N° de réf. du vendeur 9783659891038
Quantité disponible : 1 disponible(s)