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Data mining : practical machine learning tools and techniques /
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 o...
| Clasificación: | Libro Electrónico |
|---|---|
| Autores principales: | , , |
| Formato: | Electrónico eBook |
| Idioma: | Inglés |
| Publicado: |
Burlington, MA :
Morgan Kaufmann,
©2011.
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| Edición: | 3rd ed. |
| Colección: | Morgan Kaufmann series in data management systems.
|
| Temas: | |
| Acceso en línea: | Texto completo Texto completo |
MARC
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| 100 | 1 | |a Witten, I. H. |q (Ian H.), |e author. | |
| 245 | 1 | 0 | |a Data mining : |b practical machine learning tools and techniques / |c Ian H. Witten, Eibe Frank, Mark A. Hall. |
| 250 | |a 3rd ed. | ||
| 260 | |a Burlington, MA : |b Morgan Kaufmann, |c ©2011. | ||
| 300 | |a 1 online resource (xxxiii, 629 pages) : |b illustrations | ||
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| 520 | |a 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. | ||
| 505 | 0 | |a What's it all about? -- Input : concepts, instances, and attributes -- Output : knowledge representation -- Algorithms : the basic methods -- Credibility : evaluating what's been learned -- Implementations : real machine learning schemes -- Data transformation -- Ensemble learning -- Moving on : applications and beyond -- Introduction to Weka -- The explorer -- The knowledge flow interface -- The experimenter -- The command-line interface -- Embedded machine learning -- Writing new learning schemes -- Tutorial exercises for the weka explorer. | |
| 504 | |a Includes bibliographical references and index. | ||
| 590 | |a eBooks on EBSCOhost |b EBSCO eBook Subscription Academic Collection - Worldwide | ||
| 590 | |a O'Reilly |b O'Reilly Online Learning: Academic/Public Library Edition | ||
| 650 | 0 | |a Data mining. | |
| 650 | 1 | 2 | |a Data Mining |
| 650 | 6 | |a Exploration de données (Informatique) | |
| 650 | 7 | |a COMPUTERS |x Database Management |x Data Mining. |2 bisacsh | |
| 650 | 7 | |a Data mining. |2 fast |0 (OCoLC)fst00887946 | |
| 650 | 7 | |a Computers and IT. |2 eflch | |
| 650 | 7 | |a Data mining. |2 nli | |
| 650 | 7 | |a Machine learning. |2 nli | |
| 650 | 7 | |a Data mining. |2 sao | |
| 655 | 4 | |a Internet Resources. | |
| 700 | 1 | |a Frank, Eibe, |e author. | |
| 700 | 1 | |a Hall, Mark A. |q (Mark Andrew), |e author. | |
| 776 | 0 | 8 | |i Print version: |a Witten, I.H. (Ian H.). |t Data mining. |b 3rd ed. |d Burlington, MA : Morgan Kaufmann, ©2011 |z 9780123748560 |w (DLC) 2010039827 |w (OCoLC)262433473 |
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