Data Mining: Practical Machine Learning Tools and Techniques, 3/e (Paperback)
暫譯: 資料探勘:實用機器學習工具與技術,第3版(平裝本)
Ian H. Witten, Eibe Frank, Mark A. Hall
- 出版商: Morgan Kaufmann
- 出版日期: 2011-01-20
- 售價: $1,225
- 語言: 英文
- 頁數: 664
- 裝訂: Paperback
- ISBN: 0123748569
- ISBN-13: 9780123748560
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相關分類:
Machine Learning、Data-mining
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商品描述
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
商品描述(中文翻譯)
《資料探勘:實用機器學習工具與技術》提供了機器學習概念的全面基礎,以及在現實資料探勘情境中應用機器學習工具和技術的實用建議。這本備受期待的第三版是資料探勘和機器學習領域中最受推崇的著作,將教會你有關準備輸入、解釋輸出、評估結果以及成功資料探勘核心的演算法方法所需的所有知識。
全面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,包括有關資料轉換、集成學習、大型資料集、多實例學習的新材料,以及作者開發的流行 Weka 機器學習軟體的新版本。Witten、Frank 和 Hall 包含了當今經過驗證的技術以及當代研究前沿的方法。
* 提供機器學習概念的全面基礎,以及在資料探勘專案中應用工具和技術的實用建議
* 提供具體的性能改進技巧和技術,通過轉換機器學習方法中的輸入或輸出來實現
* 包含可下載的 Weka 軟體工具包,這是一組用於資料探勘任務的機器學習演算法,具有更新的互動介面。工具包中的演算法涵蓋:資料預處理、分類、回歸、聚類、關聯規則、視覺化