A Vehicle Classification Algorithm Based on Adaboost.M1

Neural network classifiers have problems of over learning, less learning, fall into curse of dimensionality or local minimum, and support vector machine classifiers also have problems of more complex operations, model selection and construction of kernel function is more difficult, and Bayesian classification only in the number of training samples tends to infinity, the training results of the model tends to true.This paper presents a vehicle classification algorithm based on Adaboost.M1.The algorithm is simple to use, and just need to find a weak classifier which′s precision slightly higher than the random prediction, without adjusting any parameters, no prior knowledge, and there is sufficient theoretical support.Finally, experimental results demonstrate the effectiveness of the vehicle classification algorithm based on Adaboost.M1.