Boosting through optimization of margin distributions
Boosting has been of great interest recently in the machine learning community because of the impressive performance for classifi- cation and regression problems. The success of boosting algorithms may be interpreted in terms of the margin theory. Recently, it has been shown that generalization error of classifiers can be obtained by explicitly taking the margin distribution of the training data into account. Most of the current boosting algorithms in practice usually optimize a convex loss...[Show more]
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|Source:||IEEE Transactions on Neural Networks|
|01_Shen_Boosting_through_optimization_2010.pdf||531.07 kB||Adobe PDF||Request a copy|
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