000			01871cam a2200349 a 4500
001			17253290
005			20230913020002.0
008			120412s2012 flua b 001 0 eng
010			_a 2012014555
020			_a9781439830031
040			_aDLC _cDLC
042			_apcc
050	0	0	_aQA278.4 _b.Z47 2012
082	0	0	_a006.31 _223 _bZHO-E
084			_aBUS061000 _aCOM021030 _aCOM037000 _2bisacsh
100	1		_aZhou, Zhi-Hua
245	1	0	_aEnsemble methods : _bfoundations and algorithms _cZhi-Hua Zhou.
260			_aBoca Raton, FL : _bTaylor & Francis, _c©2012.
300			_axiv, 222 p. : _bill. ; _c25 cm.
490	0		_aChapman & Hall/CRC machine learning & pattern recognition series
504			_aIncludes bibliographical references (p. 187-218) and index.
520			_a"This 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"--
650		0	_aMultiple comparisons (Statistics)
650		0	_aSet theory.
650		0	_aMathematical analysis.
650		7	_aBUSINESS & ECONOMICS / Statistics _2bisacsh.
650		7	_aCOMPUTERS / Database Management / Data Mining _2bisacsh.
650		7	_aCOMPUTERS / Machine Theory _2bisacsh.
906			_a7 _bcbc _corignew _d1 _eecip _f20 _gy-gencatlg
942			_2ddc _cBK _05
999			_c9633 _d9633