Data mining practical machine learning tools and techniques. [electronic resource] :

• 作者： Witten, I. H. (Ian H.)
• 其他作者：
  • Hall, Mark A.
• 其他題名：
  • Morgan Kaufmann series in data management systems
• 出版： Burlington, MA : Morgan Kaufmann
• 版本：3rd ed. /
• 叢書名： [Morgan Kaufmann series in data management systems]
• 主題： Data mining , COMPUTERS--Database Management--Data Mining. , Electronic books. , Electronic books.
• ISBN： 9780123748560 (electronic bk.) 、 0123748569 (electronic bk.) 、 9780080890364 (electronic bk.) 、 0080890369 (electronic bk.)
• FIND@SFXID: CGU
• 資料類型: 電子書
• 內容註: Includes bibliographical references and index. PART I: Introduction to Data Mining Ch 1 What's It All About? Ch 2 Input: Concepts, Instances, Attributes Ch 3 Output: Knowledge Representation Ch 4 Algorithms: The Basic Methods Ch 5 Credibility: Evaluating What's Been Learned PART II: Advanced Data Mining Ch 6 Implementations: Real Machine Learning Schemes Ch 7 Data Transformation Ch 8 Ensemble Learning Ch 9 Moving On: Applications and Beyond PART III: The Weka Data MiningWorkbench Ch 10 Introduction to Weka Ch 11 The Explorer Ch 12 The Knowledge Flow Interface Ch 13 The Experimenter Ch 14 The Command-Line Interface Ch 15 Embedded Machine Learning Ch 16 Writing New Learning Schemes Ch 17 Tutorial Exercises for the Weka Explorer. Machine generated contents note: PART I: Machine Learning Tools and Techniques. Ch 1. What's It All About? Ch 2. Input: Concepts, Instances, Attributes. Ch 3. Output: Knowledge Representation. Ch 4. Algorithms: The Basic Methods. Ch 5. Credibility: Evaluating What's Been Learned. PART II: Advanced Data Mining. Ch 6. Implementations: Real Machine Learning Schemes. Ch 7. Data Transformation. Ch 8. Ensemble Learning. Ch 9. Moving On: Applications and Beyond. PART III: The Weka Data MiningWorkbench. Ch 10. Introduction to Weka. Ch 11. The Explorer. Ch 12. The Knowledge Flow Interface. Ch 13. The Experimenter. Ch 14 The Command-Line Interface. Ch 15. Embedded Machine Learning. Ch 16. Writing New Learning Schemes. Ch 17. Tutorial Exercises for the Weka Explorer. Machine generated contents note: PART I: Machine Learning Tools and Techniques. Ch 1. What's It All About? Ch 2. Input: Concepts, Instances, Attributes. Ch 3. Output: Knowledge Representation. Ch 4. Algorithms: The Basic Methods. Ch 5. Credibility: Evaluating What's Been Learned. PART II: Advanced Data Mining. Ch 6. Implementations: Real Machine Learning Schemes. Ch 7. Data Transformation. Ch 8. Ensemble Learning. Ch 9. Moving On: Applications and Beyond. PART III: The Weka Data MiningWorkbench. Ch 10. Introduction to Weka. Ch 11. The Explorer. Ch 12. The Knowledge Flow Interface. Ch 13. The Experimenter. Ch 14 The Command-Line Interface. Ch 15. Embedded Machine Learning. Ch 16. Writing New Learning Schemes. Ch 17. Tutorial Exercises for the Weka Explorer.
• More
• 摘要註: Data Mining: Practical Machine Learning Tools and Techniques offers a thorough grounding in machine learning concepts as well as practical advice on applying machine learning tools and techniques in real-world data mining situations. This highly anticipated third edition of the most acclaimed work on data mining and machine learning will teach you everything you need to know about preparing inputs, interpreting outputs, evaluating results, and the algorithmic methods at the heart of successful data mining. Thorough updates reflect the technical changes and modernizations that have taken place in the field since the last edition, including new material on Data Transformations, Ensemble Learning, Massive Data Sets, Multi-instance Learning, plus a new version of the popular Weka machine learning software developed by the authors. Witten, Frank, and Hall include both tried-and-true techniques of today as well as methods at the leading edge of contemporary research. *Provides a thorough grounding in machine learning concepts as well as practical advice on applying the tools and techniques to your data mining projects *Offers concrete tips and techniques for performance improvement that work by transforming the input or output in machine learning methods *Includes downloadable Weka software toolkit, a collection of machine learning algorithms for data mining tasks-in an updated, interactive interface. Algorithms in toolkit cover: data pre-processing, classification, regression, clustering, association rules, visualization.
• 讀者標籤：

  需登入
• 引用連結：

  Copy Link
• 目前引用數
• Share: