9783844323146 - hybrid strategies for improving bayesian networks: applying mathematical and computational intelligence models to optimize and extend the modelling and applicability de santana, ádamo lima; renato francês, carlos; weyl costa, joão (6 resultados)
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Librería: moluna, Greven, Alemaniamoluna
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Taschenbuch. Condición: Neu. Hybrid Strategies for Improving Bayesian Networks | Applying Mathematical and Computational Intelligence Models to Optimize and Extend the Modelling and Applicability | Ádamo Lima Santana (u. a.) | Taschenbuch | 80 S. | Englisch | 2011 | LAP LAMBERT Academic Publishing | EAN 9783844323146 | Verantwor…tliche Person für die EU: BoD - Books on Demand, In de Tarpen 42, 22848 Norderstedt, info[at]bod[dot]de | Anbieter: preigu.
Hybrid Strategies for Improving Bayesian Networks: Applying Mathematical and Computational Intelligence Models to Optimize and Extend the Modelling and Applicability
Santana, Ádamo Lima, Renato Francês, Carlos, Weyl Costa, Joã
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Librería: Mispah books, Redhill, SURRE, Reino UnidoMispah books
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Paperback. Condición: Like New. LIKE NEW. SHIPS FROM MULTIPLE LOCATIONS. book.
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Librería: BuchWeltWeit Ludwig Meier e.K., Bergisch Gladbach, AlemaniaBuchWeltWeit Ludwig Meier e.K.
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Taschenbuch. Condición: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -One of the main factors for the success of data mining is related to the comprehensibility of the patterns discovered by the computational intelligence techniques; with Bayesian networks standing as one of the most prominent, when c…onsidering the easiness of knowledge interpretation achieved. Its quantitative and qualitative semantics, allied to the comprehensibility of the patterns discovered, motivates its application in the knowledge discovery process. Bayesian networks, however, like any computational intelligence technique, presents limitations and disadvantages regarding its use; amongst which we can point the learning of the structure from large datasets and the provision of inferences throughout time. This book will show extensions for the improvement of Bayesian networks, presenting strategies to improve its properties, treating aspects such as performance, as well as interpretability and use of its results; incorporating models of multiple regression for structure learning, and temporal aspects using Markov chains. The models should help users extending the range of applicability of this versatile model for new domains and tasks. 80 pp. Englisch.
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Librería: buchversandmimpf2000, Emtmannsberg, BAYE, Alemaniabuchversandmimpf2000
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Taschenbuch. Condición: Neu. This item is printed on demand - Print on Demand Titel. Neuware -One of the main factors for the success of data mining is related to the comprehensibility of the patterns discovered by the computational intelligence techniques; with Bayesian networks standing as one of the most prominent, when consi…dering the easiness of knowledge interpretation achieved. Its quantitative and qualitative semantics, allied to the comprehensibility of the patterns discovered, motivates its application in the knowledge discovery process. Bayesian networks, however, like any computational intelligence technique, presents limitations and disadvantages regarding its use; amongst which we can point the learning of the structure from large datasets and the provision of inferences throughout time. This book will show extensions for the improvement of Bayesian networks, presenting strategies to improve its properties, treating aspects such as performance, as well as interpretability and use of its results; incorporating models of multiple regression for structure learning, and temporal aspects using Markov chains. The models should help users extending the range of applicability of this versatile model for new domains and tasks.VDM Verlag, Dudweiler Landstraße 99, 66123 Saarbrücken 80 pp. Englisch.
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Librería: AHA-BUCH GmbH, Einbeck, AlemaniaAHA-BUCH GmbH
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Taschenbuch. Condición: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - One of the main factors for the success of data mining is related to the comprehensibility of the patterns discovered by the computational intelligence techniques; with Bayesian networks standing as one of the most prominent, when consid…ering the easiness of knowledge interpretation achieved. Its quantitative and qualitative semantics, allied to the comprehensibility of the patterns discovered, motivates its application in the knowledge discovery process. Bayesian networks, however, like any computational intelligence technique, presents limitations and disadvantages regarding its use; amongst which we can point the learning of the structure from large datasets and the provision of inferences throughout time. This book will show extensions for the improvement of Bayesian networks, presenting strategies to improve its properties, treating aspects such as performance, as well as interpretability and use of its results; incorporating models of multiple regression for structure learning, and temporal aspects using Markov chains. The models should help users extending the range of applicability of this versatile model for new domains and tasks.
