Articoli correlati a Relative-Fuzzy: A Novel Approach for Handling Complex...
Relative-Fuzzy: A Novel Approach for Handling Complex Ambiguity for Software Engineering of Data Mining Models - Brossura
Sinossi
Relative-Fuzzy is a new approach for handling the complex ambiguity type of uncertainty that may exist in data, for software engineering of predictive Data Mining (DM) classification models. This approach is based on a novel type of fuzzy logic which has been called Relative-Fuzzy Logic (RFL). RFL defines a new formulation of the problem of ambiguity type of uncertainty in terms of States Of Proposition (SOP). RFL describes its membership (semantic) value by using the new definition of Domain of Proposition (DOP), which is based on the relativity principle as defined by possible-worlds logic. Two types of logic; namely fuzzy logic and possible-world logic, have been mixed to produce a new membership value set that is able to handle fuzziness and multiple viewpoints at the same time, which called Relative-Fuzzy membership value set. For implementation purpose, a new architecture of Hierarchical Neural Network (HNN) called ML/RFL-Based Net along with its new learning and recalling algorithms has been developed. This new type of HNN is considered to be a RFL computation based machine.
Le informazioni nella sezione "Riassunto" possono far riferimento a edizioni diverse di questo titolo.
L'autore
Ayad Tareq Imam is an Assistant Professor of CS at Al-Isra University/Amman/Jordan, and has been teaching CS for 17 years. Dr. Ayad received his Ph.D. in Computer Science at De Montfort University /Leicester/U.K. and has number of published papers in various CS topics. He is also a reviewer in different CS and IT journals and conferences.
Le informazioni nella sezione "Su questo libro" possono far riferimento a edizioni diverse di questo titolo.
Compra nuovo
Visualizza questo articolo
EUR 63,42
EUR 9,70 per la spedizione da Germania a Italia
Destinazione, tempi e costiRisultati della ricerca per Relative-Fuzzy: A Novel Approach for Handling Complex...
Immagini fornite dal venditore
Relative-Fuzzy
Editore:
LAP LAMBERT Academic Publishing, 2011
ISBN 10: 384547212X
ISBN 13: 9783845472126
Nuovo
Brossura
Da: moluna, Greven, Germania
Valutazione del venditore 5 su 5 stelle
Condizione: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Autor/Autorin: Imam Ayad TareqAyad Tareq Imam is an Assistant Professor of CS at Al-Isra University/Amman/Jordan, and has been teaching CS for 17 years. Dr. Ayad received his Ph.D. in Computer Science at De Montfort University /Leicester/U.K. and h. Codice articolo 5484006
Quantità: Più di 20 disponibili
Immagini fornite dal venditore
Relative-Fuzzy
Editore:
LAP LAMBERT Academic Publishing Sep 2011, 2011
ISBN 10: 384547212X
ISBN 13: 9783845472126
Nuovo
Taschenbuch
Da: BuchWeltWeit Ludwig Meier e.K., Bergisch Gladbach, Germania
Valutazione del venditore 5 su 5 stelle
Taschenbuch. Condizione: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -Relative-Fuzzy is a new approach for handling the complex ambiguity type of uncertainty that may exist in data, for software engineering of predictive Data Mining (DM) classification models. This approach is based on a novel type of fuzzy logic which has been called Relative-Fuzzy Logic (RFL). RFL defines a new formulation of the problem of ambiguity type of uncertainty in terms of States Of Proposition (SOP). RFL describes its membership (semantic) value by using the new definition of Domain of Proposition (DOP), which is based on the relativity principle as defined by possible-worlds logic. Two types of logic; namely fuzzy logic and possible-world logic, have been mixed to produce a new membership value set that is able to handle fuzziness and multiple viewpoints at the same time, which called Relative-Fuzzy membership value set. For implementation purpose, a new architecture of Hierarchical Neural Network (HNN) called ML/RFL-Based Net along with its new learning and recalling algorithms has been developed. This new type of HNN is considered to be a RFL computation based machine. 236 pp. Englisch. Codice articolo 9783845472126
Quantità: 2 disponibili
Immagini fornite dal venditore
Relative-Fuzzy : A Novel Approach for Handling Complex Ambiguity for Software Engineering of Data Mining Models
Editore:
LAP LAMBERT Academic Publishing, 2011
ISBN 10: 384547212X
ISBN 13: 9783845472126
Nuovo
Taschenbuch
Da: AHA-BUCH GmbH, Einbeck, Germania
Valutazione del venditore 5 su 5 stelle
Taschenbuch. Condizione: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - Relative-Fuzzy is a new approach for handling the complex ambiguity type of uncertainty that may exist in data, for software engineering of predictive Data Mining (DM) classification models. This approach is based on a novel type of fuzzy logic which has been called Relative-Fuzzy Logic (RFL). RFL defines a new formulation of the problem of ambiguity type of uncertainty in terms of States Of Proposition (SOP). RFL describes its membership (semantic) value by using the new definition of Domain of Proposition (DOP), which is based on the relativity principle as defined by possible-worlds logic. Two types of logic; namely fuzzy logic and possible-world logic, have been mixed to produce a new membership value set that is able to handle fuzziness and multiple viewpoints at the same time, which called Relative-Fuzzy membership value set. For implementation purpose, a new architecture of Hierarchical Neural Network (HNN) called ML/RFL-Based Net along with its new learning and recalling algorithms has been developed. This new type of HNN is considered to be a RFL computation based machine. Codice articolo 9783845472126
Quantità: 1 disponibili
Immagini fornite dal venditore
Relative-Fuzzy
Editore:
LAP LAMBERT Academic Publishing Sep 2011, 2011
ISBN 10: 384547212X
ISBN 13: 9783845472126
Nuovo
Taschenbuch
Da: buchversandmimpf2000, Emtmannsberg, BAYE, Germania
Valutazione del venditore 5 su 5 stelle
Taschenbuch. Condizione: Neu. Neuware -Relative-Fuzzy is a new approach for handling the complex ambiguity type of uncertainty that may exist in data, for software engineering of predictive Data Mining (DM) classification models. This approach is based on a novel type of fuzzy logic which has been called Relative-Fuzzy Logic (RFL). RFL defines a new formulation of the problem of ambiguity type of uncertainty in terms of States Of Proposition (SOP). RFL describes its membership (semantic) value by using the new definition of Domain of Proposition (DOP), which is based on the relativity principle as defined by possible-worlds logic. Two types of logic; namely fuzzy logic and possible-world logic, have been mixed to produce a new membership value set that is able to handle fuzziness and multiple viewpoints at the same time, which called Relative-Fuzzy membership value set. For implementation purpose, a new architecture of Hierarchical Neural Network (HNN) called ML/RFL-Based Net along with its new learning and recalling algorithms has been developed. This new type of HNN is considered to be a RFL computation based machine.Books on Demand GmbH, Überseering 33, 22297 Hamburg 236 pp. Englisch. Codice articolo 9783845472126
Quantità: 2 disponibili