Boosting算法理论与应用研究
张文生; 于廷照
2016-03
发表期刊中国科技大学学报
卷号46期号:3页码:222-230
其他摘要Boosting is one of the most popular ensemble algorithms in machine learning,and it has been widely used in machine learning and pattern recognition. There are mainly two frameworks of Boosting,learnable theory and statistical theory. Boosting was first proposed from the theory of weak learnability which illustrates the theory of boosting a group of weak learners into a strong learner. After a finite number of iterations,the combination of weak learners could be boosted into any accuracy on the training set,and the only requirement for a weak learner is that the accuracy be slightly better than a random guess. From the statistical point of view,Boosting is an additive model,and
the equivalence between these two models has already been proved. The theory of weak learnability is reviewed from the PAC perspective,and the challenges Boosting may face are presented,includeing effectiveness for high dimension data and the Margin theory. Then,various Boosting algorithms are discussed from the above two viewpoints and their new applications with Boosting framework. Finally,the future of Boosting is discussed.
关键词Boosting 弱可学习理论 Margin理论 集成学习 Adaboost
文献类型期刊论文
条目标识符http://ir.ia.ac.cn/handle/173211/11831
专题精密感知与控制研究中心_人工智能与机器学习
通讯作者张文生
作者单位中国科学院自动化研究所
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张文生,于廷照. Boosting算法理论与应用研究[J]. 中国科技大学学报,2016,46(3):222-230.
APA 张文生,&于廷照.(2016).Boosting算法理论与应用研究.中国科技大学学报,46(3),222-230.
MLA 张文生,et al."Boosting算法理论与应用研究".中国科技大学学报 46.3(2016):222-230.
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