期刊文献+
任意字段
题名或关键词
题名
关键词
文摘
作者
第一作者
机构
刊名
分类号
参考文献
作者简介
基金资助
栏目信息

Boosting视角 被引量：2

The Analge of View of Boosting
下载PDF
导出
摘要 AdaBoost是Boosting家族中的最基础的代表算法。本文主要介绍了AdaBoost的泛化错误分析及其与结构风险最小化和VC维、支持向量机及margin理论的关系,并从游戏理论和统计学视点分别对AdaBoost进行了理解和解释,以期提供Boosting的一个较为全面的视角。 AdaBoost is the most important fundamental algorihtm in the family of Boosting algorithms. This paper an- alyzes the generalization error of boosting and its relationship with structural risk minimization and VC dimension, Support Vector Machine and margin theory. Besides,AdaBoost is interpreted from the view point of game theory and statistics respectively,in order to provide an over all view of Boosting for promoting its further study.
作者 涂承胜 陆玉昌
机构地区 重庆三峡学院计算机科学系 清华大学计算机科学技术系
出处 《计算机科学》 CSCD 北大核心 2005年第5期140-143,共4页 Computer Science
基金 重庆市教委科技项目(编号:031104)资助.
关键词 BOOSTING ADABOOST 视角 结构风险最小化 支持向量机 错误分析 游戏理论 VC维 统计学 算法 视点 Generalization error Structural risk minimization VC dimension Support vector machine Game theory
分类号 TP301.6 [自动化与计算机技术—计算机系统结构] H315.9 [语言文字—英语]
  • 相关文献

参考文献22

  • 1Schapire R E. The strength of weak learnability. Machine Learning,1990,5(2) :197～227
  • 2Freund Y,Schapire R E. A decision-theoretic generalization of online learning and an application to boosting. Journal of Computer and System Science,1997,55(1) :119～139
  • 3Valiant L G. A theory of the learnable. Communications of the ACM,1984,27(11) :1134-1142
  • 4Kearns M J,Vazirani L G. Learning Boolean formulae or finite automata is as hard as factoring: [Technical Report TR-14-88].Harvard University Aiken Computation Laboratory,Aug. 1988
  • 5Kearns M J, Vazirani L G. Cryptographic limitations on learning Boolean formulae and finite automata. Journal of the Association for Computing Machinery, 1994,41 (1): 67～ 95
  • 6Freund Y. Boosting a weak learning algorithm by majority. Information and computation, 1994,141 (2): 256～285
  • 7Dietterich T G,Bakiri G. Solving multiclass learning problems via error-correcting output codes. Journal of Artificial Intelligence Research, 1995,2 : 263～286
  • 8Schapire R E, Singer Y. Using output codes to boost multiclass learning problems. In: Machine Learning : Proc. of the Fourteenth Intl. Conf. 1997. 313～321
  • 9Schapire R E,Singer Y. Improved boosting algorithms using confidence-related predictions. In:Proc. of the eleventh Annual Conf.on Computational Learning Theory,1998.80～91
  • 10Friedman J,Hastie T,Tibshirani R. Additive logistic regression:a statistical view of boosting: [Technical Report]. 1998

同被引文献15

  • 1闫明松,周志华.代价敏感分类算法的实验比较[J].模式识别与人工智能,2005,18(5):628-635. 被引量：14
  • 2Mason L,Baxter J,Bartlett P,et al. Boosting algorithms as gra dient deseent[C] // Neural Information Processing Systems 12 Cambridge: MIT Press, 2000 : 512-518.
  • 3Friedman J, Hastie T, Tibshirani R. Additive logistic regression a statistical view of boosting[J]. The Annals of Statistics, 2000 28(2) : 337-407.
  • 4Seiffert C,Khoshgoftaar T M, Hulse J V, et al. RUSBoost: Im proving classification performance when training data is skewed [C]//Proceedings of 19th International Conference on Pattern Recognition. Washington DC: IEEE Computer Society, 2008:1-4.
  • 5Guo H Y,Viktor H L. Learning from imbalanced data sets with boosting and data generation: the DataBoost-IM approach[J]. SIGKDD Explorations, 2004,6 ( 1 ):30-39.
  • 6Sun Y,Kamel M S,Wong A K C, et al. Cost-sensitive boosting for classification of imbalanced data[J].Pattern Recognition, 2007,40(12) :3358-3378.
  • 7Li Q J, Mao Y B, Wang Z Q, et al. Cost-sensitive boosting: fit ring an additive asymmetric logistic regression model[C]//Proceedings of the 1st Asian Conference on Machine Learning: Advances in Machine Learning ( ACML ' 09 ). Berlin: Springer, 2009 : 234-247.
  • 8Masnadi-Shirazi H, Vaseoneelos N. Cost-sensitive boosting[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence,2010,33(2) :294-309.
  • 9Newman D, Hettich S, Blake C, et al. UCI repository of machine learning data bases[DB/OL], http://www, ics. uci. edu/-mlearn/MLRepository, html, 2011-05-01.
  • 10Hanley J A,McNeil B J. The meaning and use of the area under a receiver operating characteristic (ROC) curve [J]. Radiology, 1982,143(1):29-36.

引证文献2

二级引证文献19

计算机科学
2005年 第5期

相关作者

内容加载中请稍等...

相关机构

内容加载中请稍等...

相关主题

内容加载中请稍等...

浏览历史

内容加载中请稍等...
;
使用帮助 返回顶部