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Network boosting (NB) is an ensemble learning method that combines weak learners together based on a network and can learn the target hypothesis asymptotically. The experiment results show that NB can improve the classification accuracy significantly compared to Bagging and AdaBoost. We compare the accumulative margin distributions of the three ensemble learning methods and find that NB draws merit...
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