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Sinopsis
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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Reseña del editor
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.
Biografía del autor
Dr. 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 language are areas of interest.
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Evolutionary Machine Learning in Linguistic Knowledge Extraction
Publicado por
LAP LAMBERT Academic Publishing, 2016
ISBN 10: 3659891037
ISBN 13: 9783659891038
Nuevo
Tapa blanda
Librería: moluna, Greven, Alemania
Calificación del vendedor: 5 de 5 estrellas
Condición: 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. Nº de ref. del artículo: 158248679
Cantidad disponible: Más de 20 disponibles
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Evolutionary Machine Learning in Linguistic Knowledge Extraction
Publicado por
LAP LAMBERT Academic Publishing Mai 2016, 2016
ISBN 10: 3659891037
ISBN 13: 9783659891038
Nuevo
Taschenbuch
Librería: BuchWeltWeit Ludwig Meier e.K., Bergisch Gladbach, Alemania
Calificación del vendedor: 5 de 5 estrellas
Taschenbuch. Condición: 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. Nº de ref. del artículo: 9783659891038
Cantidad disponible: 2 disponibles
Imagen del vendedor
Evolutionary Machine Learning in Linguistic Knowledge Extraction
Publicado por
LAP LAMBERT Academic Publishing, 2016
ISBN 10: 3659891037
ISBN 13: 9783659891038
Nuevo
Taschenbuch
Librería: AHA-BUCH GmbH, Einbeck, Alemania
Calificación del vendedor: 5 de 5 estrellas
Taschenbuch. Condición: 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. Nº de ref. del artículo: 9783659891038
Cantidad disponible: 1 disponibles
Imagen del vendedor
Evolutionary Machine Learning in Linguistic Knowledge Extraction
Publicado por
LAP LAMBERT Academic Publishing Mai 2016, 2016
ISBN 10: 3659891037
ISBN 13: 9783659891038
Nuevo
Taschenbuch
Librería: buchversandmimpf2000, Emtmannsberg, BAYE, Alemania
Calificación del vendedor: 5 de 5 estrellas
Taschenbuch. Condición: 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. Nº de ref. del artículo: 9783659891038
Cantidad disponible: 2 disponibles