Applying computational intelligence (46 resultados)

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 | Verantwortliche Person für die EU: preigu GmbH & Co. KG, Lengericher Landstr. 19, 49078 Osnabrück, mail[at]preigu[dot]de | Anbieter: preigu.…

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Buch. Condición: Neu. Druck auf Anfrage Neuware - Printed after ordering - In theory, there is no difference between theory and practice. But, in practice, there is. Jan L. A. van de Snepscheut The ow of academic ideas in the area of computational intelligence has penetrated industry with tremendous speed and persistence. Thousands of applications have proved the practical potential of fuzzy logic, neural networks, evolutionary com- tation, swarm intelligence, and intelligent agents even before their theoretical foundation is completely understood. And the popularity is rising. Some software vendors have pronounced the new machine learning gold rush to 'Transfer Data into Gold'. New buzzwords like 'data mining', 'genetic algorithms', and 'swarm optimization' have enriched the top executives' vocabulary to make them look more 'visionary' for the 21st century. The phrase 'fuzzy math' became political jargon after being used by US President George W. Bush in one of the election debates in the campaign in 2000. Even process operators are discussing the perf- mance of neural networks with the same passion as the performance of the Dallas Cowboys. However, for most of the engineers and scientists introducing computational intelligence technologies into practice, looking at the growing number of new approaches, and understanding their theoretical principles and potential for value creation becomes a more and more dif cult task.…

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Taschenbuch. Condición: Neu. Druck auf Anfrage Neuware - Printed after ordering - In theory, there is no difference between theory and practice. But, in practice, there is. Jan L. A. van de Snepscheut The ow of academic ideas in the area of computational intelligence has penetrated industry with tremendous speed and persistence. Thousands of applications have proved the practical potential of fuzzy logic, neural networks, evolutionary com- tation, swarm intelligence, and intelligent agents even before their theoretical foundation is completely understood. And the popularity is rising. Some software vendors have pronounced the new machine learning gold rush to Transfer Data into Gold . New buzzwords like data mining , genetic algorithms , and swarm optimization have enriched the top executives vocabulary to make them look more visionary for the 21st century. The phrase fuzzy math became political jargon after being used by US President George W. Bush in one of the election debates in the campaign in 2000. Even process operators are discussing the perf- mance of neural networks with the same passion as the performance of the Dallas Cowboys. However, for most of the engineers and scientists introducing computational intelligence technologies into practice, looking at the growing number of new approaches, and understanding their theoretical principles and potential for value creation becomes a more and more dif cult task.…

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Taschenbuch. Condición: Neu. Applying Computational Intelligence | How to Create Value | Arthur Kordon | Taschenbuch | xxii | Englisch | 2014 | Springer | EAN 9783642424267 | Verantwortliche Person für die EU: Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg, juergen[dot]hartmann[at]springer[dot]com | Anbieter: preigu. …

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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 considering 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.…

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