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Fuzzy Modeling and Genetic Algorithms for Data Mining and Exploration 在线电子书 pdf 下载 txt下载 epub 下载 mobi 下载 2024

Fuzzy Modeling and Genetic Algorithms for Data Mining and Exploration
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简体网页||繁体网页
Earl Cox 作者
Morgan Kaufmann
译者
2005-1 出版日期
530 页数
USD 63.95 价格
Paperback
丛书系列
9780121942755 图书编码

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Fuzzy Modeling and Genetic Algorithms for Data Mining and Exploration 在线电子书 epub 下载 mobi 下载 pdf 下载 txt 下载 2024

Fuzzy Modeling and Genetic Algorithms for Data Mining and Exploration 在线电子书 epub 下载 mobi 下载 pdf 下载 txt 下载 2024

Fuzzy Modeling and Genetic Algorithms for Data Mining and Exploration 在线电子书 pdf 下载 txt下载 epub 下载 mobi 下载 2024


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Fuzzy Modeling and Genetic Algorithms for Data Mining and Exploration 在线电子书 图书描述

"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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