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《Journal of Southeast University(Natural Science Edition)》 2011-04
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Theory deduction of AdaBoost classification

Yan Chao1 Wang Yuanqing1 Li Jiuxue2 Zhang Zhaoyang3(1School of Electric Science and Engineering,Nanjing University,Nanjing 210093,China)(2School of Information Science and Engineering,Southeast University,Nanjing 210096,China)(3Key Laboratory of Advanced Display and System Application of Ministry of Education,Shanghai University,Shanghai 200444,China)  
AdaBoost two-classification and AdaBoost multi-classification lack mutual theory principals,so the unity of AdaBoost algorithm could not be represented theorically.To solve this problem,firstly,the connection of AdaBoost algorithm and Bayes Inference is probed;secondly,the training process and relative parameters of AdaBoost algorithm are analyzed quantitatively;thirdly,with fundamental inequality principals,the extension process of AdaBoost algorithm from two-classification application to multi-classification application is reasoned.Two intrinsic theories are summarized and proved: if the sum of some non-negative numbers is fixed,their product will become smaller when their values difference become greater;arithmetic average of some non-negative numbers is greater than their geometric average.In addition,some improvements to two-classification and multi-classification applications are suggested.
【Fund】: 国家自然科学基金重点资助项目(608320036);; 新型显示技术及应用集成教育部重点实验室资助项目(P200902);; 南京大学研究生创新基金资助项目(2011CL03);; 江苏省研究生培养创新工程资助项目
【CateGory Index】: TP301.6
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