Data Mining : Practical Machine Learning Tools and Techniques, 4/e (Paperback)

Ian H. Witten, Eibe Frank, Mark A. Hall, Christopher J. Pal

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商品描述

Data Mining: Practical Machine Learning Tools and Techniques, Fourth Edition, offers a thorough grounding in machine learning concepts, along with practical advice on applying these tools and techniques in real-world data mining situations. This highly anticipated fourth edition of the most acclaimed work on data mining and machine learning teaches readers everything they need to know to get going, from preparing inputs, interpreting outputs, evaluating results, to the algorithmic methods at the heart of successful data mining approaches.

 Extensive updates reflect the technical changes and modernizations that have taken place in the field since the last edition, including substantial new chapters on probabilistic methods and on deep learning. Accompanying the book is a new version of the popular WEKA machine learning software from the University of Waikato. Authors Witten, Frank, Hall, and Pal include today's techniques coupled with the methods at the leading edge of contemporary research. • Powerpoint slides for Chapters 1-12. This is a very comprehensive teaching resource, with many PPT slides covering each chapter of the book
• Online Appendix on the Weka workbench; again a very comprehensive learning aid for the open source software that goes with the book
• Table of contents, highlighting the many new sections in the 4th edition, along with reviews of the 1st edition, errata, etc.

商品描述(中文翻譯)

《資料探勘:實用機器學習工具與技術,第四版》提供了對機器學習概念的全面介紹,並提供了在實際資料探勘情境中應用這些工具和技術的實用建議。這本备受期待的第四版是關於資料探勘和機器學習最受讚譽的著作,教讀者從準備輸入、解釋輸出、評估結果,到成功資料探勘方法的核心算法,所需了解的一切。

全面更新反映了自上一版以來該領域發生的技術變化和現代化,包括全新的概率方法和深度學習章節。書中附帶的是來自Waikato大學的受歡迎的WEKA機器學習軟體的新版本。作者Witten、Frank、Hall和Pal結合了當今的技術和當代研究的前沿方法。

• 第1-12章的Powerpoint幻燈片。這是一個非常全面的教學資源,包含了每章的許多PPT幻燈片。
• Weka工作台的線上附錄;這也是一個非常全面的學習輔助工具,用於與本書相關的開源軟體。
• 目錄,突出了第四版中的許多新章節,以及對第一版的評論、勘誤等。

目錄大綱

Part I: Introduction to data mining
 Chapter 1. What’s it all about?
 Chapter 2. Input: Concepts, instances, attributes
 Chapter 3. Output: Knowledge representation
 Chapter 4. Algorithms: The basic methods
 Chapter 5. Credibility: Evaluating what’s been learned

Part II: More advanced machine learning schemes
 Chapter 6. Trees and rules
 Chapter 7. Extending instance-based and linear models
 Chapter 8. Data transformations
 Chapter 9. Probabilistic methods
 Chapter 10. Deep learning
 Chapter 11. Beyond supervised and unsupervised learning
 Chapter 12. Ensemble learning
 Chapter 13. Moving on: applications and beyond Abstract

Appendix A. Theoretical foundations
 Appendix B. The WEKA workbench
 References
 Index

目錄大綱(中文翻譯)

第一部分:資料探勘介紹
第1章:這是什麼?
第2章:輸入:概念、實例、屬性
第3章:輸出:知識表示
第4章:演算法:基本方法
第5章:可信度:評估所學到的內容

第二部分:更進階的機器學習方案
第6章:樹和規則
第7章:擴展基於實例和線性模型
第8章:資料轉換
第9章:機率方法
第10章:深度學習
第11章:超越監督和非監督學習
第12章:集成學習
第13章:前進:應用和更多摘要

附錄A:理論基礎
附錄B:WEKA工作台
參考文獻
索引