Data Mining Ebook Tooltip Ebooks kunnen worden gelezen op uw computer en op daarvoor geschikte e-readers. Practical Machine Learning Tools and Techniques, 3e
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Auteur:
Ian H. Witten
Eibe Frank
- Engels
- E-book
- 2370004236827
- 560 pagina's
- Adobe ePub
Ian H. Witten
"Ian H. Witten is a computer scientist at the University of Waikato, New Zealand. He is a Chartered Engineer with the Institute of Electrical Engineers in London who graduated from the University of Cambridge with a BA and MA (First Class Honours) in mathematics in 1969 and an M.Sc. in mathematics and computer science from the University of Calgary, where he was a Commonwealth Scholar, in 1970. He received his Ph.D., Learning to Control in 1976 from the University of Essex, England (Electrical Engineering Science). Witten is a co-creator of the sequitur algorithm and original creator of the WEKA software package for data mining.
(Bron: Wikipedia. Beschikbaar onder de licentie Creative Commons Naamsvermelding/Gelijk delen.)"
(Bron: Wikipedia. Beschikbaar onder de licentie Creative Commons Naamsvermelding/Gelijk delen.)"
Samenvatting
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
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
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