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Ajouter au panierTaschenbuch. Etat : Neu. Classification and Modeling with Linguistic Information Granules | Advanced Approaches to Linguistic Data Mining | Hisao Ishibuchi (u. a.) | Taschenbuch | xii | Englisch | 2010 | Springer-Verlag GmbH | EAN 9783642058608 | Verantwortliche Person für die EU: Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg, juergen[dot]hartmann[at]springer[dot]com | Anbieter: preigu.
Edité par Springer-Verlag Berlin and Heidelberg GmbH and Co. KG, DE, 2004
ISBN 10 : 3540207678 ISBN 13 : 9783540207672
Langue: anglais
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Ajouter au panierHardback. Etat : New. 2005 ed. Many approaches have already been proposed for classification and modeling in the literature. These approaches are usually based on mathematical mod els. Computer systems can easily handle mathematical models even when they are complicated and nonlinear (e.g., neural networks). On the other hand, it is not always easy for human users to intuitively understand mathe matical models even when they are simple and linear. This is because human information processing is based mainly on linguistic knowledge while com puter systems are designed to handle symbolic and numerical information. A large part of our daily communication is based on words. We learn from various media such as books, newspapers, magazines, TV, and the Inter net through words. We also communicate with others through words. While words play a central role in human information processing, linguistic models are not often used in the fields of classification and modeling. If there is no goal other than the maximization of accuracy in classification and model ing, mathematical models may always be preferred to linguistic models. On the other hand, linguistic models may be chosen if emphasis is placed on interpretability.
Edité par Springer Berlin Heidelberg, 2010
ISBN 10 : 3642058604 ISBN 13 : 9783642058608
Langue: anglais
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Ajouter au panierTaschenbuch. Etat : Neu. Druck auf Anfrage Neuware - Printed after ordering - Many approaches have already been proposed for classification and modeling in the literature. These approaches are usually based on mathematical mod els. Computer systems can easily handle mathematical models even when they are complicated and nonlinear (e.g., neural networks). On the other hand, it is not always easy for human users to intuitively understand mathe matical models even when they are simple and linear. This is because human information processing is based mainly on linguistic knowledge while com puter systems are designed to handle symbolic and numerical information. A large part of our daily communication is based on words. We learn from various media such as books, newspapers, magazines, TV, and the Inter net through words. We also communicate with others through words. While words play a central role in human information processing, linguistic models are not often used in the fields of classification and modeling. If there is no goal other than the maximization of accuracy in classification and model ing, mathematical models may always be preferred to linguistic models. On the other hand, linguistic models may be chosen if emphasis is placed on interpretability.
Edité par Springer Berlin Heidelberg, 2004
ISBN 10 : 3540207678 ISBN 13 : 9783540207672
Langue: anglais
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Ajouter au panierGebunden. Etat : New. Clear illustration how fuzzy logic and neural networks are utilized for extracting linguistic knowledgeMany figures and simulation resultsReference for researchers and practitioners in data mining, fuzzy systems and neural networksBe.
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Ajouter au panierPaperback. Etat : Brand New. 320 pages. 9.00x6.00x0.73 inches. In Stock.
Edité par Springer, Berlin, Springer Berlin Heidelberg, Springer, 2004
ISBN 10 : 3540207678 ISBN 13 : 9783540207672
Langue: anglais
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Ajouter au panierBuch. Etat : Neu. Neuware - Many approaches have already been proposed for classification and modeling in the literature. These approaches are usually based on mathematical mod els. Computer systems can easily handle mathematical models even when they are complicated and nonlinear (e.g., neural networks). On the other hand, it is not always easy for human users to intuitively understand mathe matical models even when they are simple and linear. This is because human information processing is based mainly on linguistic knowledge while com puter systems are designed to handle symbolic and numerical information. A large part of our daily communication is based on words. We learn from various media such as books, newspapers, magazines, TV, and the Inter net through words. We also communicate with others through words. While words play a central role in human information processing, linguistic models are not often used in the fields of classification and modeling. If there is no goal other than the maximization of accuracy in classification and model ing, mathematical models may always be preferred to linguistic models. On the other hand, linguistic models may be chosen if emphasis is placed on interpretability.
Edité par Springer-Verlag Berlin and Heidelberg GmbH and Co. KG, DE, 2004
ISBN 10 : 3540207678 ISBN 13 : 9783540207672
Langue: anglais
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Ajouter au panierHardback. Etat : New. 2005 ed. Many approaches have already been proposed for classification and modeling in the literature. These approaches are usually based on mathematical mod els. Computer systems can easily handle mathematical models even when they are complicated and nonlinear (e.g., neural networks). On the other hand, it is not always easy for human users to intuitively understand mathe matical models even when they are simple and linear. This is because human information processing is based mainly on linguistic knowledge while com puter systems are designed to handle symbolic and numerical information. A large part of our daily communication is based on words. We learn from various media such as books, newspapers, magazines, TV, and the Inter net through words. We also communicate with others through words. While words play a central role in human information processing, linguistic models are not often used in the fields of classification and modeling. If there is no goal other than the maximization of accuracy in classification and model ing, mathematical models may always be preferred to linguistic models. On the other hand, linguistic models may be chosen if emphasis is placed on interpretability.
Edité par Springer Berlin Heidelberg Feb 2010, 2010
ISBN 10 : 3642058604 ISBN 13 : 9783642058608
Langue: anglais
Vendeur : BuchWeltWeit Ludwig Meier e.K., Bergisch Gladbach, Allemagne
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Ajouter au panierTaschenbuch. Etat : Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -Many approaches have already been proposed for classification and modeling in the literature. These approaches are usually based on mathematical mod els. Computer systems can easily handle mathematical models even when they are complicated and nonlinear (e.g., neural networks). On the other hand, it is not always easy for human users to intuitively understand mathe matical models even when they are simple and linear. This is because human information processing is based mainly on linguistic knowledge while com puter systems are designed to handle symbolic and numerical information. A large part of our daily communication is based on words. We learn from various media such as books, newspapers, magazines, TV, and the Inter net through words. We also communicate with others through words. While words play a central role in human information processing, linguistic models are not often used in the fields of classification and modeling. If there is no goal other than the maximization of accuracy in classification and model ing, mathematical models may always be preferred to linguistic models. On the other hand, linguistic models may be chosen if emphasis is placed on interpretability. 320 pp. Englisch.
Edité par Springer Berlin Heidelberg, 2010
ISBN 10 : 3642058604 ISBN 13 : 9783642058608
Langue: anglais
Vendeur : moluna, Greven, Allemagne
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Ajouter au panierEtat : New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Clear illustration how fuzzy logic and neural networks are utilized for extracting linguistic knowledgeMany figures and simulation resultsReference for researchers and practitioners in data mining, fuzzy systems and neural networksBe.
Edité par Springer Berlin Heidelberg, Springer Berlin Heidelberg Feb 2010, 2010
ISBN 10 : 3642058604 ISBN 13 : 9783642058608
Langue: anglais
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Ajouter au panierTaschenbuch. Etat : Neu. This item is printed on demand - Print on Demand Titel. Neuware -Many approaches have already been proposed for classification and modeling in the literature. These approaches are usually based on mathematical mod els. Computer systems can easily handle mathematical models even when they are complicated and nonlinear (e.g., neural networks). On the other hand, it is not always easy for human users to intuitively understand mathe matical models even when they are simple and linear. This is because human information processing is based mainly on linguistic knowledge while com puter systems are designed to handle symbolic and numerical information. A large part of our daily communication is based on words. We learn from various media such as books, newspapers, magazines, TV, and the Inter net through words. We also communicate with others through words. While words play a central role in human information processing, linguistic models are not often used in the fields of classification and modeling. If there is no goal other than the maximization of accuracy in classification and model ing, mathematical models may always be preferred to linguistic models. On the other hand, linguistic models may be chosen if emphasis is placed on interpretability.Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg 320 pp. Englisch.
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Ajouter au panierEtat : New. Print on Demand pp. 320 217 Illus.
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Ajouter au panierEtat : New. PRINT ON DEMAND pp. 320.