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Data mining [[electronic resource] ] : practical machine learning tools and techniques / / Ian H. Witten, Eibe Frank, Mark A. Hall
Data mining [[electronic resource] ] : practical machine learning tools and techniques / / Ian H. Witten, Eibe Frank, Mark A. Hall
Autore Witten I. H (Ian H.)
Edizione [3rd ed.]
Pubbl/distr/stampa Amsterdam, : Elsevier/Morgan Kaufmann, 2011
Descrizione fisica 1 online resource (665 p.) : ill
Disciplina 006.3/12
Altri autori (Persone) FrankEibe
HallMark A
Collana The Morgan Kaufmann Series in Data Management Systems
Soggetto topico Data mining
Soggetto genere / forma Electronic books.
ISBN 1-282-95388-5
9786612953880
0-08-089036-9
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Nota di contenuto 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.
Record Nr. UNINA-9910459549103321
Witten I. H (Ian H.)  
Amsterdam, : Elsevier/Morgan Kaufmann, 2011
Materiale a stampa
Lo trovi qui: Univ. Federico II
Opac: Controlla la disponibilità qui
Data mining [[electronic resource] ] : practical machine learning tools and techniques / / Ian H. Witten, Eibe Frank, Mark A. Hall
Data mining [[electronic resource] ] : practical machine learning tools and techniques / / Ian H. Witten, Eibe Frank, Mark A. Hall
Autore Witten I. H (Ian H.)
Edizione [3rd ed.]
Pubbl/distr/stampa Amsterdam, : Elsevier/Morgan Kaufmann, 2011
Descrizione fisica 1 online resource (665 p.) : ill
Disciplina 006.3/12
Altri autori (Persone) FrankEibe
HallMark A
Collana The Morgan Kaufmann Series in Data Management Systems
Soggetto topico Data mining
ISBN 1-282-95388-5
9786612953880
0-08-089036-9
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Nota di contenuto 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.
Record Nr. UNINA-9910785305703321
Witten I. H (Ian H.)  
Amsterdam, : Elsevier/Morgan Kaufmann, 2011
Materiale a stampa
Lo trovi qui: Univ. Federico II
Opac: Controlla la disponibilità qui
Data mining [[electronic resource] ] : practical machine learning tools and techniques / / Ian H. Witten, Eibe Frank, Mark A. Hall
Data mining [[electronic resource] ] : practical machine learning tools and techniques / / Ian H. Witten, Eibe Frank, Mark A. Hall
Autore Witten I. H (Ian H.)
Edizione [3rd ed.]
Pubbl/distr/stampa Amsterdam, : Elsevier/Morgan Kaufmann, 2011
Descrizione fisica 1 online resource (665 p.) : ill
Disciplina 006.3/12
Altri autori (Persone) FrankEibe
HallMark A
Collana The Morgan Kaufmann Series in Data Management Systems
Soggetto topico Data mining
ISBN 1-282-95388-5
9786612953880
0-08-089036-9
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Nota di contenuto 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.
Record Nr. UNINA-9910822335803321
Witten I. H (Ian H.)  
Amsterdam, : Elsevier/Morgan Kaufmann, 2011
Materiale a stampa
Lo trovi qui: Univ. Federico II
Opac: Controlla la disponibilità qui