2013, ISBN: 9783659429095

[ED: Taschenbuch / Paperback], [PU: LAP Lambert Academic Publishing], Collaborative filtering (CF), a very successful recommender system, is one of the applications of data mining for inc… Mehr…

[ED: Taschenbuch / Paperback], [PU: LAP Lambert Academic Publishing], Collaborative filtering (CF), a very successful recommender system, is one of the applications of data mining for incomplete data. The main objective of CF is to make accurate recommendations from highly sparse user rating data. My contributions to this research topic include proposing the frameworks of imputation-boosted collaborative filtering (IBCF) and imputed neighborhood based collaborative filtering (INCF). We also proposed a model-based CF technique, TAN-ELR CF, and two hybrid CF algorithms, sequential mixture CF and joint mixture CF. Empirical results show that our proposed CF algorithms have very good predictive performances. In the investigation of applying imputation techniques in mining incomplete data, we proposed imputation-helped classifiers, and VCI predictors (voting on classifications from imputed learning sets), both of which resulted in significant improvement in classification performance for incomplete data over conventional machine learned classifiers. The main imputation techniques involved in these algorithms include EM (expectation maximization) and BMI (Bayesian multiple imputation)., DE, [SC: 0.00], Neuware, gewerbliches Angebot, H: 220mm, B: 150mm, 164, Selbstabholung und Barzahlung, PayPal, Offene Rechnung, Banküberweisung, Internationaler Versand<

2013, ISBN: 3659429090

[EAN: 9783659429095], Neubuch, [PU: LAP Lambert Academic Publishing Jul 2013], This item is printed on demand - Print on Demand Neuware - Collaborative filtering (CF), a very successful r… Mehr…

[EAN: 9783659429095], Neubuch, [PU: LAP Lambert Academic Publishing Jul 2013], This item is printed on demand - Print on Demand Neuware - Collaborative filtering (CF), a very successful recommender system, is one of the applications of data mining for incomplete data. The main objective of CF is to make accurate recommendations from highly sparse user rating data. My contributions to this research topic include proposing the frameworks of imputation-boosted collaborative filtering (IBCF) and imputed neighborhood based collaborative filtering (INCF). We also proposed a model-based CF technique, TAN-ELR CF, and two hybrid CF algorithms, sequential mixture CF and joint mixture CF. Empirical results show that our proposed CF algorithms have very good predictive performances. In the investigation of applying imputation techniques in mining incomplete data, we proposed imputation-helped classifiers, and VCI predictors (voting on classifications from imputed learning sets), both of which resulted in significant improvement in classification performance for incomplete data over conventional machine learned classifiers. The main imputation techniques involved in these algorithms include EM (expectation maximization) and BMI (Bayesian multiple imputation). 164 pp. Englisch, Books<

2013, ISBN: 9783659429095

Collaborative filtering (CF), a very successful recommender system, is one of the applications of data mining for incomplete data. The main objective of CF is to make accurate recommendat… Mehr…

Collaborative filtering (CF), a very successful recommender system, is one of the applications of data mining for incomplete data. The main objective of CF is to make accurate recommendations from highly sparse user rating data. My contributions to this research topic include proposing the frameworks of imputation-boosted collaborative filtering (IBCF) and imputed neighborhood based collaborative filtering (INCF). We also proposed a model-based CF technique, TAN-ELR CF, and two hybrid CF algorithms, sequential mixture CF and joint mixture CF. Empirical results show that our proposed CF algorithms have very good predictive performances. In the investigation of applying imputation techniques in mining incomplete data, we proposed imputation-helped classifiers, and VCI predictors (voting on classifications from imputed learning sets), both of which resulted in significant improvement in classification performance for incomplete data over conventional machine learned classifiers. The main imputation techniques involved in these algorithms include EM (expectation maximization) and BMI (Bayesian multiple imputation). Buch (fremdspr.) Xiaoyuan Su Taschenbuch, LAP LAMBERT Academic Publishing, 12.07.2013, LAP LAMBERT Academic Publishing, 2013<

ISBN: 3659429090

Gebundene Ausgabe

Collaborative Filtering ab 51.9 € als Taschenbuch: Using Machine Learning and Statistical Techniques. Aus dem Bereich: Bücher, English, International, Gebundene Ausgaben, Medien > Bücher,… Mehr…

Collaborative Filtering ab 51.9 € als Taschenbuch: Using Machine Learning and Statistical Techniques. Aus dem Bereich: Bücher, English, International, Gebundene Ausgaben, Medien > Bücher, LAP Lambert Academic Publishing<

ISBN: 3659429090

Collaborative Filtering ab 51.9 EURO Using Machine Learning and Statistical Techniques Medien > Bücher