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[null,"1.","Record Nr.",null,"UNINA9910785305703321"]
[null,null,"Autore",null,"Witten I. H (Ian H.)"]
[null,null,"Titolo",null,"Data mining [[electronic resource] ] : practical machine learning tools and techniques / / Ian H. Witten, Eibe Frank, Mark A. Hall"]
[null,null,"Pubbl/distr/stampa",null,"Amsterdam, : Elsevier/Morgan Kaufmann, 2011"]
[null,null,"ISBN",null,"1-282-95388-5"]
[{"$nulls":4},"9786612953880"]
[{"$nulls":4},"0-08-089036-9"]
[null,null,"Edizione",null,"[3rd ed.]"]
[null,null,"Descrizione fisica",null,"1 online resource (665 p.) : ill"]
[null,null,"Collana",null,"The Morgan Kaufmann Series in Data Management Systems"]
[null,null,"Disciplina",null,"006.3/12"]
[null,null,"Soggetti",null,"Data mining"]
[null,null,"Lingua di pubblicazione",null,"Inglese"]
[null,null,"Formato",null,"Materiale a stampa"]
[null,null,null,"Livello bibliografico","Monografia"]
[null,null,null,"Note generali","Description based upon print version of record"]
[null,null,null,"Nota di bibliografia","Includes bibliographical references and index."]
[null,null,"Nota di contenuto",null,"Part I. Machine learning tools and techniques: 1. What's it all about?; 2. Input: concepts, instances, and attributes; 3. Output: knowledge representation; 4. Algorithms: the basic methods; 5. Credibility: evaluating what's been learned -- Part II. Advanced Data Mining: 6. Implementations: real machine learning schemes; 7. Data transformation; 8. Ensemble learning; 9. Moving on: applications and beyond -- Part III. The Weka Data MiningWorkbench: 10. Introduction to Weka; 11. The explorer -- 12. The knowledge flow interface; 13. The experimenter; 14 The command-line interface; 15. Embedded machine learning; 16. Writing new learning schemes; 17. Tutorial exercises for the weka explorer."]
[null,null,null,"Sommario/riassunto","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 modernizatio"]