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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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Evolutionary Machine Learning in Linguistic Knowledge Extraction
Edité par
LAP LAMBERT Academic Publishing, 2016
ISBN 10 : 3659891037
ISBN 13 : 9783659891038
Neuf
Couverture souple
Vendeur : moluna, Greven, Allemagne
Évaluation du vendeur 5 sur 5 étoiles
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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Evolutionary Machine Learning in Linguistic Knowledge Extraction
Edité par
LAP LAMBERT Academic Publishing, 2016
ISBN 10 : 3659891037
ISBN 13 : 9783659891038
Neuf
Taschenbuch
Vendeur : AHA-BUCH GmbH, Einbeck, Allemagne
Évaluation du vendeur 5 sur 5 étoiles
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)
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Evolutionary Machine Learning in Linguistic Knowledge Extraction
Edité par
LAP LAMBERT Academic Publishing Mai 2016, 2016
ISBN 10 : 3659891037
ISBN 13 : 9783659891038
Neuf
Taschenbuch
Vendeur : BuchWeltWeit Ludwig Meier e.K., Bergisch Gladbach, Allemagne
Évaluation du vendeur 5 sur 5 étoiles
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
Quantité disponible : 2 disponible(s)
Image fournie par le vendeur
Evolutionary Machine Learning in Linguistic Knowledge Extraction
Edité par
LAP LAMBERT Academic Publishing Mai 2016, 2016
ISBN 10 : 3659891037
ISBN 13 : 9783659891038
Neuf
Taschenbuch
Vendeur : buchversandmimpf2000, Emtmannsberg, BAYE, Allemagne
Évaluation du vendeur 5 sur 5 étoiles
Taschenbuch. Etat : Neu. 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
Quantité disponible : 2 disponible(s)