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In order to solve the problem of overfitting in AdaBoost, we propose a novel AdaBoost algorithm using K-means clustering. AdaBoost is known as an effective method for improving the performance of base classifiers both theoretically and empirically. However, previous studies have shown that AdaBoost is prone to overfitting in overlapped classes. In order to(More)
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