东南大学学报（自然科学版）2011，Vol.41Issue(4)：700-705,6.DOI:10.3969/j.issn.1001-0505.2011.04.009
AdaBoost分类问题的理论推导
Theory deduction of AdaBoost classification
摘要
Abstract
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.关键词
多分类/AdaBoost算法/归一化因子/贝叶斯推理Key words
multi-classification/ AdaBoost algorithm/ normalization factor/ Bayes inference分类
信息技术与安全科学引用本文复制引用
严超,王元庆,李久雪,张兆扬..AdaBoost分类问题的理论推导[J].东南大学学报（自然科学版）,2011,41(4):700-705,6.基金项目
国家自然科学基金重点资助项目(608320036) （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) （P200902）
南京大学研究生创新基金资助项目(2011CL03) （2011CL03）
江苏省研究生培养创新工程资助项目. （）
