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- 371pages
- 13 heures de lecture
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This book 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. Inside, you'll learn all you need to know about preparing inputs, interpreting outputs, evaluating results, and the algorithmic methods at the heart of successful data mining--including both tried-and-true techniques of the past and Java-based methods at the leading edge of contemporary research. If you're involved at any level in the work of extracting usable knowledge from large collections of data, this clearly written and effectively illustrated book will prove an invaluable resource. Complementing the authors' instruction is a fully functional platform-independent Java software system for machine learning, available for download. Apply it to the sample data sets provided to refine your data mining skills, apply it to your own data to discern meaningful patterns and generate valuable insights, adapt it for your specialized data mining applications, or use it to develop your own machine learning schemes.
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Data Mining, Ian H. Witten, Eibe Frank
- Langue
- Année de publication
- 1999
- product-detail.submit-box.info.binding
- (souple)
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- Titre
- Data Mining
- Sous-titre
- Practical Machine Learning Tools and Techniques with Java Implementations
- Langue
- Anglais
- Auteurs
- Ian H. Witten, Eibe Frank
- Éditeur
- Morgan Kaufmann
- Publié
- 1999
- Format
- souple
- Pages
- 371
- ISBN10
- 1558605525
- ISBN13
- 9781558605527
- Séries
- Mots clés
- Nonfiction, Manuels et guides, Informatique & Internet, États-Unis, Technologie, Intelligence Artificielle, Analyse de données, Java, Apprentissage automatique
- Description
- This book 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. Inside, you'll learn all you need to know about preparing inputs, interpreting outputs, evaluating results, and the algorithmic methods at the heart of successful data mining--including both tried-and-true techniques of the past and Java-based methods at the leading edge of contemporary research. If you're involved at any level in the work of extracting usable knowledge from large collections of data, this clearly written and effectively illustrated book will prove an invaluable resource. Complementing the authors' instruction is a fully functional platform-independent Java software system for machine learning, available for download. Apply it to the sample data sets provided to refine your data mining skills, apply it to your own data to discern meaningful patterns and generate valuable insights, adapt it for your specialized data mining applications, or use it to develop your own machine learning schemes.