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Pattern Recognition Algorithms for Data Mining: Scalability, Knowledge Discovery and Soft Granular Computing (Chapman & Hall/CRC Computer Science & Data Analysis) 🔍
Sankar K Pal; Pabitra Mitra, PhD Chapman and Hall/CRC, CRC Press (Unlimited), Boca Raton, Fla, 2004
英语 [en] · PDF · 15.3MB · 2004 · 📗 未知类型的图书 · 🚀/ia · Save
描述
This valuable text addresses different pattern recognition (PR) tasks in a unified framework with both theoretical and experimental results. Tasks covered include data condensation, feature selection, case generation, clustering/classification, and rule generation and evaluation. Organized into eight chapters, the book begins by introducing PR, data mining, and knowledge discovery concepts. The authors proceed to analyze the tasks of multi-scale data condensation and dimensionality reduction. Then they explore the problem of learning with support vector machine (SVM), and conclude by highlighting the significance of granular computing for different mining tasks in a soft paradigm.
备选作者
Pal, Sankar K; Mitra, Pabitra, PhD
备选作者
Sankar K. Pal and Pabitra Mitra
备用出版商
Boca Raton, Fla.: Chapman & Hall/CRC
备用出版商
CRC Press LLC
备用版本
Chapman & Hall/CRC Computer Science & Data Analysis, Boca Raton, Fla, ©2004
备用版本
United States, United States of America
备用版本
Boca Raton [etc.], United States, 2004
备用版本
Boca Raton, Fla. ; London, ©2004
备用版本
Boca Raton, Fla, Florida, 2004
备用版本
May 27, 2004
备用版本
1, US, 2004
元数据中的注释
Includes bibliographical references (p. 215-236) and index
元数据中的注释
Указ.
Includes bibliographical references (p. 215-236)
元数据中的注释
РГБ
元数据中的注释
Russian State Library [rgb] MARC:
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备用描述
<p>Pattern Recognition Algorithms for Data Mining addresses different pattern recognition (PR) tasks in a unified framework with both theoretical and experimental results. Tasks covered include data condensation, feature selection, case generation, clustering/classification, and rule generation and evaluation. This volume presents various theories, methodologies, and algorithms, using both classical approaches and hybrid paradigms. The authors emphasize large datasets with overlapping, intractable, or nonlinear boundary classes, and datasets that demonstrate granular computing in soft frameworks.</p>
<p>Organized into eight chapters, the book begins with an introduction to PR, data mining, and knowledge discovery concepts. The authors analyze the tasks of multi-scale data condensation and dimensionality reduction, then explore the problem of learning with support vector machine (SVM). They conclude by highlighting the significance of granular computing for different mining tasks in a soft paradigm.</p>
备用描述
Annotation Pattern Recognition Algorithms for Data Mining addresses different pattern recognition (PR) tasks in a unified framework with both theoretical and experimental results. Tasks covered include data condensation, feature selection, case generation, clustering/classification, and rule generation and evaluation. This volume presents various theories, methodologies, and algorithms, using both classical approaches and hybrid paradigms. The authors emphasize large datasets with overlapping, intractable, or nonlinear boundary classes, and datasets that demonstrate granular computing in soft frameworks. Organized into eight chapters, the book begins with an introduction to PR, data mining, and knowledge discovery concepts. The authors analyze the tasks of multi-scale data condensation and dimensionality reduction, then explore the problem of learning with support vector machine (SVM). They conclude by highlighting the significance of granular computing for different mining tasks in a soft paradigm
备用描述
xxix, 244 p. : 25 cm
Includes bibliographical references (p. 215-236) and index
开源日期
2024-07-01
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