中图分类法:	TP181 版次：
著者:	Schapire, Robert E.
题名:	Boosting : [ foundations and algorithms /] / ,
出版发行:	出版地： Cambridge, MA : 出版社： MIT Press, 出版日期： c2012.
载体形态:	xv, 526 p. : ill. ; 24 cm.
内容提要:	Boosting is an approach to machine learning based on the idea of creating a highly accurate predictor by combining many weak and inaccurate "rules of thumb." A remarkably rich theory has evolved around boosting, with connections to a range of topics, including statistics, game theory, convex optimization, and information geometry. Boosting algorithms have also enjoyed practical success in such fields as mysterious, controversial, even paradoxical. This book, written by the inventors of the method, brings together, organizes, simplifies, adn substantially extends two decades of research on boosting, presenting both theory and applications in a way that is accessible to readers form diverse backgrounds while also pr