Computational learning probabil reason de gammerman (2 resultados)

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  • Idioma: Inglés

    Editorial: John Wiley & Sons, 1996

    0471962791 / 9780471962793

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    Librería: moluna, Greven, Alemaniamoluna

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    Condición: Nuevo

    EUR 322,24

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    Cantidad disponible: Más de 20 disponibles

    Gebunden. Condición: New. This text is devoted to two interrelated techniques used in solving some important problems in machine intelligence and pattern recognition.&Uumlber den AutorA. Gammerman is the editor of Computational Learning and Prob.

  • Idioma: Inglés

    Editorial: John Wiley & Sons Aug 1996, 1996

    0471962791 / 9780471962793

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    Librería: AHA-BUCH GmbH, Einbeck, AlemaniaAHA-BUCH GmbH

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    Condición: Nuevo

    EUR 410,54

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    Cantidad disponible: 2 disponibles

    Buch. Condición: Neu. Neuware - Providing a unified coverage of the latest research and applications methods and techniques, this book is devoted to two interrelated techniques for solving some important problems in machine intelligence and pattern recognition, namely probabilistic reasoning and computational learning. The contributions in this volume describe and explore the current developments in computer science and theoretical statistics which provide computational probabilistic models for manipulating knowledge found in industrial and business data. These methods are very efficient for handling complex problems in medicine, commerce and finance. Part I covers Generalisation Principles and Learning and describes several new inductive principles and techniques used in computational learning. Part II describes Causation and Model Selection including the graphical probabilistic models that exploit the independence relationships presented in the graphs, and applications of Bayesian networks to multivariate statistical analysis. Part III includes case studies and descriptions of Bayesian Belief Networks and Hybrid Systems. Finally, Part IV on Decision-Making, Optimization and Classification describes some related theoretical work in the field of probabilistic reasoning. Statisticians, IT strategy planners, professionals and researchers with interests in learning, intelligent databases and pattern recognition and data processing for expert systems will find this book to be an invaluable resource. Real-life problems are used to demonstrate the practical and effective implementation of the relevant algorithms and techniques.