A New Weak Classifier Training Method for AdaBoost Algorithm
一种新的改进AdaBoost弱分类器训练算法

AdaBoost is a very popular classification algorithm on machine leaning.By studying the characteristics of the weak classifier,this paper proposes two new methods to calculate the threshold and bias of the weak classifier.The two methods make the correct rate of weak classifier larger than 50%,assure the convergence of AdaBoost training when the weak classifier reach a certain number.Simulation experiments show when the error rate is in an acceptable range,the algorithms using fewer weak classifiers will be able to guarantee the strong classifier to maintain a high correct rate.