02927nam 2200721Ia 450 991080809220332120200520144314.0978042915109504291510989781439830055143983005310.1201/b12207 (CKB)2670000000210853(EBL)952003(OCoLC)798535723(SSID)ssj0000677440(PQKBManifestationID)11469824(PQKBTitleCode)TC0000677440(PQKBWorkID)10694638(PQKB)11392930(OCoLC)796675544(Au-PeEL)EBL952003(CaPaEBR)ebr10574375(CaONFJC)MIL581225(OCoLC)1350744941(OCoLC-P)1350744941(CaSebORM)9781439830055(MiAaPQ)EBC952003(OCoLC)1280138403(FINmELB)ELB142738(EXLCZ)99267000000021085320120412d2012 uy 0engur|n|---|||||txtccrEnsemble methods foundations and algorithms /Zhi-Hua Zhou1st ed.Boca Raton, FL Taylor & Francis20121 online resource (234 p.)Chapman & Hall/CRC machine learning & pattern recognition seriesA Chapman & Hall book.9781439830031 1439830037 Includes bibliographical references and index.Front Cover; Preface; Notations; Contents; 1. Introduction; 2. Boosting; 3. Bagging; 4. Combination Methods; 5. Diversity; 6. Ensemble Pruning; 7. Clustering Ensembles; 8. Advanced Topics; ReferencesThis comprehensive book presents an in-depth and systematic introduction to ensemble methods for researchers in machine learning, data mining, and related areas. It helps readers solve modem problems in machine learning using these methods. The author covers the spectrum of research in ensemble methods, including such famous methods as boosting, bagging, and rainforest, along with current directions and methods not sufficiently addressed in other books. Chapters explore cutting-edge topics, such as semi-supervised ensembles, cluster ensembles, and comprehensibility, as well as successful applications--Provided by publisher.Chapman & Hall/CRC machine learning & pattern recognition series.Machine learningMathematicsAlgorithmsMachine learningMathematics.Algorithms.006.3/1BUS061000COM021030COM037000bisacshZhou Zhi-HuaPh. D.849299MiAaPQMiAaPQMiAaPQBOOK9910808092203321Ensemble methods4118505UNINA