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Design for Fast Adaboost with Feature Selection

Since the original Adaboost algorithm is very time-consuming in training, we have designed an improved Adaboost, which adds a process of feature selection to the original Adaboost algorithm. After each round of training, we retain the features whose error rate to classify the samples are relatively low and remove the features with high error rate. In this way the time for the next training is reduced, and the whole algorithm is accelerated.