Data Mining : Practical Machine Learning Tools and Techniques, 4/e (Paperback)
暫譯: 資料探勘：實用機器學習工具與技術，第4版（平裝本）

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

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

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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）》提供了機器學習概念的全面基礎，並提供了在現實資料探勘情境中應用這些工具和技術的實用建議。這本備受期待的第四版是資料探勘和機器學習領域中最受讚譽的著作，教導讀者從準備輸入、解釋輸出、評估結果到成功資料探勘方法核心的演算法方法所需的所有知識。

廣泛的更新反映了自上版以來該領域發生的技術變化和現代化，包括有關機率方法和深度學習的新章節。隨書附贈的是來自懷卡托大學的流行 WEKA 機器學習軟體的新版本。作者 Witten、Frank、Hall 和 Pal 包含了當今的技術，並結合了當代研究的前沿方法。
• 第1至第12章的 PowerPoint 幻燈片。這是一個非常全面的教學資源，包含許多涵蓋書中每一章的 PPT 幻燈片。
• Weka 工作台的線上附錄；同樣是一本非常全面的學習輔助工具，適用於與書籍搭配的開源軟體。
• 目錄，突顯第四版中的許多新部分，以及第一版的評價、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)

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

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