02755oam 2200649I 450 991079049480332120200520144314.00-429-15109-81-4398-3005-310.1201/b12207 (CKB)2670000000210853(EBL)952003(OCoLC)798535723(SSID)ssj0000677440(PQKBManifestationID)11469824(PQKBTitleCode)TC0000677440(PQKBWorkID)10694638(PQKB)11392930(OCoLC)796675544(MiAaPQ)EBC952003(Au-PeEL)EBL952003(CaPaEBR)ebr10574375(CaONFJC)MIL581225(OCoLC)1350744941(OCoLC-P)1350744941(CaSebORM)9781439830055(EXLCZ)99267000000021085320180331d2012 uy 0engur|n|---|||||txtccrEnsemble methods foundations and algorithms /Zhi-Hua ZhouBoca Raton, Fla. :CRC Press,2012.1 online resource (234 p.)Chapman & Hall/CRC machine learning & pattern recognition seriesA Chapman & Hall book.1-4398-3003-7 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.,849299MiAaPQMiAaPQMiAaPQBOOK9910790494803321Ensemble methods3720665UNINA