000			01800cam a2200289 a 4500
005			20260112063323.0
010			_a2012-014555
020			_a9781439830031 (hardback)
039			_a202110061116 _bstaff _c201508172104 _d staff _c201506090947 _d staff _y 201506090945 _z staff
082			_a006.31 _bZHO [ Shelf 73 ]
100			_aZhou, Zhi-Hua, _cPh. D.
245			_aEnsemble methods : _bfoundations and algorithms / _cZhi-Hua Zhou.
260			_aBoca Raton, FL : _bTaylor & Francis, _c2012.
300			_axiv, 222 p. : _bill. ; _c25 cm.
490			_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"-- _cProvided by publisher.
650			_aMultiple comparisons (Statistics)
650			_aSet theory.
650			_aMathematical analysis.
650			_aBUSINESS & ECONOMICS / Statistics _2 bisacsh.
650			_aCOMPUTERS / Database Management / Data Mining _2 bisacsh.
650			_aCOMPUTERS / Machine Theory _2 bisacsh.
991			_aVIRTUA40
991			_aVTLSSORT0080*0100*0200*0820*1000*2450*2600*3000*4900*5040*5200*6500*6501*6502*6503*6504*6505*9992
909			_a5338
999			_c4798 _d4798