Seller: Romtrade Corp., STERLING HEIGHTS, MI, U.S.A.
Condition: New. This is a Brand-new US Edition. This Item may be shipped from US or any other country as we have multiple locations worldwide.
Brand new book. Fast ship. Please provide full street address as we are not able to ship to P O box address.
Seller: Books Puddle, New York, NY, U.S.A.
Condition: New. pp. 320.
Seller: Majestic Books, Hounslow, United Kingdom
Condition: New. pp. 320 Illus.
Seller: Biblios, Frankfurt am main, HESSE, Germany
Condition: New. pp. 320.
Condition: New.
Condition: New.
Condition: New.
Seller: GreatBookPricesUK, Woodford Green, United Kingdom
Condition: New.
Condition: New. In.
Condition: New. In.
Seller: GreatBookPricesUK, Woodford Green, United Kingdom
Condition: As New. Unread book in perfect condition.
Seller: Mispah books, Redhill, SURRE, United Kingdom
Hardcover. Condition: Like New. LIKE NEW. SHIPS FROM MULTIPLE LOCATIONS. book.
Condition: As New. Unread book in perfect condition.
Seller: Books Puddle, New York, NY, U.S.A.
Condition: New. pp. 320.
Published by Springer-Verlag GmbH, 2010
ISBN 10: 3642058604 ISBN 13: 9783642058608
Language: English
Seller: preigu, Osnabrück, Germany
Taschenbuch. Condition: 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.
Published by Springer-Verlag Berlin and Heidelberg GmbH and Co. KG, DE, 2004
ISBN 10: 3540207678 ISBN 13: 9783540207672
Language: English
Seller: Rarewaves.com USA, London, LONDO, United Kingdom
Hardback. Condition: 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.
Published by Springer Berlin Heidelberg, 2010
ISBN 10: 3642058604 ISBN 13: 9783642058608
Language: English
Seller: AHA-BUCH GmbH, Einbeck, Germany
Taschenbuch. Condition: 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.
Published by Springer Berlin Heidelberg, 2004
ISBN 10: 3540207678 ISBN 13: 9783540207672
Language: English
Seller: moluna, Greven, Germany
Gebunden. Condition: 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.
Paperback. Condition: Brand New. 320 pages. 9.00x6.00x0.73 inches. In Stock.
Published by Springer-Verlag Berlin and Heidelberg GmbH and Co. KG, DE, 2004
ISBN 10: 3540207678 ISBN 13: 9783540207672
Language: English
Seller: Rarewaves.com UK, London, United Kingdom
Hardback. Condition: 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.
Published by Springer, Berlin, Springer Berlin Heidelberg, Springer, 2004
ISBN 10: 3540207678 ISBN 13: 9783540207672
Language: English
Seller: AHA-BUCH GmbH, Einbeck, Germany
Buch. Condition: 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.
Published by Springer Berlin Heidelberg Feb 2010, 2010
ISBN 10: 3642058604 ISBN 13: 9783642058608
Language: English
Seller: BuchWeltWeit Ludwig Meier e.K., Bergisch Gladbach, Germany
Taschenbuch. Condition: 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.
Published by Springer Berlin Heidelberg, 2010
ISBN 10: 3642058604 ISBN 13: 9783642058608
Language: English
Seller: moluna, Greven, Germany
Condition: 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.
Published by Springer Berlin Heidelberg, Springer Berlin Heidelberg Feb 2010, 2010
ISBN 10: 3642058604 ISBN 13: 9783642058608
Language: English
Seller: buchversandmimpf2000, Emtmannsberg, BAYE, Germany
Taschenbuch. Condition: 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.
Seller: Majestic Books, Hounslow, United Kingdom
Condition: New. Print on Demand pp. 320 217 Illus.
Seller: Biblios, Frankfurt am main, HESSE, Germany
Condition: New. PRINT ON DEMAND pp. 320.