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Data mining : practical machine learning tools and techniques /
As with any burgeoning technology that enjoys commercial attention, the use of data mining is surrounded by a great deal of hype. Exaggerated reports tell of secrets that can be uncovered by setting algorithms loose on oceans of data. But there is no magic in machine learning, no hidden power, no al...
| Clasificación: | Libro Electrónico |
|---|---|
| Autor principal: | |
| Otros Autores: | |
| Formato: | Electrónico eBook |
| Idioma: | Inglés |
| Publicado: |
Amsterdam ; Boston, MA :
Morgan Kaufman,
2005.
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| Edición: | Second edition. |
| Colección: | Morgan Kaufmann series in data management systems.
|
| Temas: | |
| Acceso en línea: | Texto completo |
MARC
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| 100 | 1 | |a Witten, I. H. |q (Ian H.) | |
| 245 | 1 | 0 | |a Data mining : |b practical machine learning tools and techniques / |c Ian H. Witten, Eibe Frank. |
| 250 | |a Second edition. | ||
| 264 | 1 | |a Amsterdam ; |a Boston, MA : |b Morgan Kaufman, |c 2005. | |
| 264 | 4 | |c ©2005 | |
| 300 | |a 1 online resource (xxxi, 525 pages) : |b illustrations | ||
| 336 | |a text |b txt |2 rdacontent | ||
| 337 | |a computer |b c |2 rdamedia | ||
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| 490 | 1 | |a Morgan Kaufmann series in data management systems | |
| 504 | |a Includes bibliographical references (pages 485-503) and index. | ||
| 588 | 0 | |a Print version record. | |
| 505 | 0 | |a pt. I. Machine learning tools and techniques. 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 -- Transformations : engineering the input and output -- Moving on : extensions and applications -- pt. II. The Weka machine learning workbench. Introduction to Weka -- The Explorer -- The Knowledge Flow interface -- The Experimenter -- The command-line interface -- Embedded machine learning -- Writing new learning schemes. | |
| 520 | |a As with any burgeoning technology that enjoys commercial attention, the use of data mining is surrounded by a great deal of hype. Exaggerated reports tell of secrets that can be uncovered by setting algorithms loose on oceans of data. But there is no magic in machine learning, no hidden power, no alchemy. Instead there is an identifiable body of practical techniques that can extract useful information from raw data. This book describes these techniques and shows how they work. The book is a major revision of the first edition that appeared in 1999. While the basic core remains the same, it has been updated to reflect the changes that have taken place over five years, and now has nearly double the references. The highlights for the new edition include thirty new technique sections; an enhanced Weka machine learning workbench, which now features an interactive interface; comprehensiv inforation on neural networks; a new section on Bayesian networks; plus much more. Offering a thorough grounding in machine learning concepts as well as practical advice on applying the tools and techniques, inside you'll find: Algorithmic methods at the heart of successful data mining -- including tried and true techniques as well as leading edge methods; Performance improvement techniques that work by transforming the input or output; Downloadable Weka, a collection of machine learning algorithms for data mining tasks, including tools for data pre-processing, classification, regression, clustering, association rules, and visualization -- in a new, interactive interface. -- |c Back cover. | ||
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| 650 | 0 | |a Data mining. | |
| 650 | 2 | |a Data Mining | |
| 650 | 6 | |a Exploration de données (Informatique) | |
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| 650 | 7 | |a Descoberta de conhecimento. |2 larpcal | |
| 650 | 7 | |a Mineração de dados. |2 larpcal | |
| 700 | 1 | |a Frank, Eibe. | |
| 776 | 0 | 8 | |i Print version: |a Witten, I.H. (Ian H.). |t Data mining. |b 2nd ed. |d Amsterdam ; Boston, MA : Morgan Kaufman, 2005 |z 0120884070 |w (DLC) 2005043385 |w (OCoLC)58451668 |
| 830 | 0 | |a Morgan Kaufmann series in data management systems. | |
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