AdaBoost algorithm based on fitted weak classifier

Ada Boost algorithm was proposed to minimize the accuracy caused by weak classifiers by minimizing the training error rate, and the single threshold was weaker and difficult to converge. The AdaBoost algorithm based on the fitted weak classifier was proposed. Firstly, the mapping relationship between eigenvalues and marker values was established. The least squares method was introduced to solve the fitting polynomial function, and the continuous fitting values were converted into discrete categorical values, thereby obtaining a weak classifier. From the many classifiers obtained, the classifier with smaller fitting error was selected as the weak classifier to form a new AdaBoost strong classifier. The UCI dataset and the MIT face image database were selected for experimental verification. Compared with the traditional Discrete-AdaBoost algorithm, the training speed of the improved algorithm was increased by an order of magnitude. And the face detection rate can reach 96.59%.