关于Real AdaBoost算法的分析与改进

采用一种新的技术,对Real AdaBoost算法的有效性、误差估计、算法流程和弱分类器训练进行了分析和证明。证明了可用加权组合弱分类器对Real AdaBoost算法进行改进,并得到了近似最佳组合系数;指出Real AdaBoost算法的样本权值调整和弱分类器训练方法的真实目的是确保弱分类器的独立性;基于Bayes统计推断对Real AdaBoost算法进行了多分类推广,得到了算法公式和误差估计,给出了便于使用的弱分类器训练简化方法。得到了Gentle AdaBoost算法的误差估计公式。UCI数据实验验证了所提算法和改进算法的效果。

Analysis and Improvement on Real AdaBoost Algorithm

The effectiveness, error formula, algorithm flow, and weak classifiers training of Real AdaBoost algorithm are analyzed and proved by a new technique. Real AdaBoost algorithm is improved by weighted combination of weak classifiers and the approximately best combination coefficients are obtained. It is proved that the function of sample weight adjusting method and weak classifiers training method is to guarantee the independence of weak classifiers. Multi-class Real AdaBoost algorithm is proposed based on Bayes statistics deduction. The formula of algorithm and the estimation of classification error are discussed. The training method of weak classifiers is simplified. The estimation of classification error of Gentle AdaBoost is obtained. The effectiveness of the proposed algorithms is verified by the experiment on UCI dataset.