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Fuzzy Modeling and Genetic Algorithms for Data Mining and Exploration 在線電子書 pdf 下載 txt下載 epub 下載 mobi 下載 2024

Fuzzy Modeling and Genetic Algorithms for Data Mining and Exploration
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Earl Cox 作者
Morgan Kaufmann
譯者
2005-1 出版日期
530 頁數
USD 63.95 價格
Paperback
叢書系列
9780121942755 圖書編碼

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Fuzzy Modeling and Genetic Algorithms for Data Mining and Exploration 在線電子書 epub 下載 mobi 下載 pdf 下載 txt 下載 2024

Fuzzy Modeling and Genetic Algorithms for Data Mining and Exploration 在線電子書 epub 下載 pdf 下載 mobi 下載 txt 下載 2024

Fuzzy Modeling and Genetic Algorithms for Data Mining and Exploration 在線電子書 pdf 下載 txt下載 epub 下載 mobi 下載 2024


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Fuzzy Modeling and Genetic Algorithms for Data Mining and Exploration 在線電子書 著者簡介

Fuzzy Modeling and Genetic Algorithms for Data Mining and Exploration 在線電子書 著者簡介

Fuzzy Modeling and Genetic Algorithms for Data Mining and Exploration 在線電子書 pdf 下載 txt下載 epub 下載 mobi 在線電子書下載

Fuzzy Modeling and Genetic Algorithms for Data Mining and Exploration 在線電子書 圖書描述

"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 you'll 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 don't 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, this work: 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; and, presents examples in C, C++, Java, and easy-to-understand pseudo-code. It also features an extensive online component, including sample code and a complete data mining workbench.

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