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Fuzzy modeling and genetic ...
Fuzzy Modeling and Genetic Algorithms for Data Mining and Exploration is a handbook for analysts, engineers, and managers involved in developing data mining models in business and government. As youll discover, fuzzy systems are extraordinarily valuable tools for representing and manipulating all kinds of data, and genetic algorithms and evolutionary programming techniques drawn from biology provide the most effective means for designing and tuning these systems. You dont need a background in fuzzy modeling or genetic algorithms to benefit, for this book provides it, along with detailed instruction in methods that you can immediately put to work in your own projects. The author provides many diverse examples and also an extended example in which evolutionary strategies are used to create a complex scheduling system. * Written to provide analysts, engineers, and managers with the background and specific instruction needed to develop and implement more effective data mining systems. * Helps you to understand the trade-offs implicit in various models and model architectures. * Provides extensive coverage of fuzzy SQL querying, fuzzy clustering, and fuzzy rule induction. * Lays out a roadmap for exploring data, selecting model system measures, organizing adaptive feedback loops, selecting a model configuration, implementing a working model, and validating the final model. * In an extended example, applies evolutionary programming techniques to solve a complicated scheduling problem. * Presents examples in C, C++, Java, and easy-to-understand pseudo-code. * Extensive online component, including sample code and a complete data mining workbench
Monografía
monografia Rebiun08276016 https://catalogo.rebiun.org/rebiun/record/Rebiun08276016 m o d cr cn| 070806s2005 ne a ob 001 0 eng d 9780121942755 0121942759 9780080470597 0080470599 UPVA 996887705303706 UAM 991007820271004211 UPM 991005522798004212 CBUC 991005179455506711 CBUC 991009627129406719 CBUC 991010369547506709 CBUC 991013399083806708 CBUC 991003517431706714 CBUC 991004007685006713 CBUC 991000729485906712 UCAR 991007777847804213 CBUC 991010369547506709 CBUC 991010369547506709 OPELS. eng. OPELS. OKU. OCLCQ. NT. YDXCP. MERUC. IDEBK. DEBBG. OCLCQ. UNAV 006.3/12 22 Cox, Earl Fuzzy modeling and genetic algorithms for data mining and exploration Recurso electrónico] Earl Cox Amsterdam Boston Elsevier/Morgan Kaufmann c2005 Amsterdam Boston Amsterdam Boston Elsevier/Morgan Kaufmann xxi, 530 p. il xxi, 530 p. EBSCO Academic eBook Collection Complete The Morgan Kaufmann series in data management systems Incluye referencias bibliográficas e índice Foundations and ideas -- Principal model types -- Approaches to model building -- Fundamental concepts of fuzzy logic -- Fundamental concepts of fuzzy systems -- Fuzzy SQL and intelligent queries -- Fuzzy clustering -- Fuzzy rule induction -- Fundamental concepts of genetic algorithms -- Genetic resource scheduling optimization -- Genetic tuning of fuzzy models Fuzzy Modeling and Genetic Algorithms for Data Mining and Exploration is a handbook for analysts, engineers, and managers involved in developing data mining models in business and government. As youll discover, fuzzy systems are extraordinarily valuable tools for representing and manipulating all kinds of data, and genetic algorithms and evolutionary programming techniques drawn from biology provide the most effective means for designing and tuning these systems. You dont need a background in fuzzy modeling or genetic algorithms to benefit, for this book provides it, along with detailed instruction in methods that you can immediately put to work in your own projects. The author provides many diverse examples and also an extended example in which evolutionary strategies are used to create a complex scheduling system. * Written to provide analysts, engineers, and managers with the background and specific instruction needed to develop and implement more effective data mining systems. * Helps you to understand the trade-offs implicit in various models and model architectures. * Provides extensive coverage of fuzzy SQL querying, fuzzy clustering, and fuzzy rule induction. * Lays out a roadmap for exploring data, selecting model system measures, organizing adaptive feedback loops, selecting a model configuration, implementing a working model, and validating the final model. * In an extended example, applies evolutionary programming techniques to solve a complicated scheduling problem. * Presents examples in C, C++, Java, and easy-to-understand pseudo-code. * Extensive online component, including sample code and a complete data mining workbench Forma de acceso: World Wide Web