Data Mining : Practical Machine Learning Tools and Techniques
Frank, Eibe, Hall, Mark A., Witten, Ian H.
ISBN 10: 0123748569 ISBN 13: 9780123748560
Verlag: Elsevier Science & Technology, 2011
Gebraucht
Softcover
Verkäufer
Better World Books Ltd, Dunfermline, Vereinigtes Königreich
Verkäuferbewertung 5 von 5 Sternen
AbeBooks-Verkäufer seit 13. Oktober 2008
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Beschreibung
Beschreibung:
Former library copy. Pages intact with possible writing/highlighting. Binding strong with minor wear. Dust jackets/supplements may not be included. Includes library markings. Stock photo provided. Product includes identifying sticker. Better World Books: Buy Books. Do Good. Bestandsnummer des Verkäufers 4867336-6
Inhaltsangabe:
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.
Über die Autorin bzw. den Autor: Ian H. Witten is a professor of computer science at the University of Waikato in New Zealand. He directs the New Zealand Digital Library research project. His research interests include information retrieval, machine learning, text compression, and programming by demonstration. He received an MA in Mathematics from Cambridge University, England; an MSc in Computer Science from the University of Calgary, Canada; and a PhD in Electrical Engineering from Essex University, England. He is a fellow of the ACM and of the Royal Society of New Zealand. He has published widely on digital libraries, machine learning, text compression, hypertext, speech synthesis and signal processing, and computer typography. He has written several books, the latest being Managing Gigabytes (1999) and Data Mining (2000), both from Morgan Kaufmann.
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Bibliografische Details
Titel: Data Mining : Practical Machine Learning ...
Verlag: Elsevier Science & Technology
Erscheinungsdatum: 2011
Einband: Softcover
Zustand: Very Good
Auflage: 3rd Edition.