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Detalles del Título

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Título Data mining [recurso electrónico]: practical machine learning tools and techniques / Ian H. Witten, Eibe Frank, Mark A. Hall. - 3rd editionLibros / En línea - Libros
Autor(es) Witten, Ian H. (Autor)
Frank, Eibe (Autor)
Hall, Mark Andrew (Autor)
Publicación Burlington, Massachusetts : Morgan Kaufmann, ©2011
Descripción Física 1 recurso en línea (xxxiii, 629 páginas) : ilustraciones
Idioma Inglés;
Series [Morgan Kaufmann series in data management systems]
ISBN 9780123748560 (electronic bk.)
9780080890364 (electronic bk.)


Clasificación(es) 006.312
INGENIERIA INDUSTRIAL
Departamento de Ingeniería Industrial
Materia(s) Data mining; Database Management - Data mining; Libros electrónicos;
Nota(s) Incluye referencias bibliográficas e índice.
CONTENIDO: 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
Títulos Relacionados Data mining: practical machine learning tools and techniques / Ian H. Witten, Eibe Frank, Mark A. Hall, Christopher J. Pal. - Fourth Edition.
Resumen 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.
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 Biblioteca Universidad Icesi 006.312/W829dDisponibleEbook