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System and method for fusing data from different information sources with shared-sampling distribution based boosting
System and method for fusing data from different information sources with shared-sampling distribution based boosting
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机译:用于基于共享采样分布的增强融合来自不同信息源的数据的系统和方法
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摘要
A boosting—based method and system for fusing a set of classifiers that performs classification using weak learners trained on different views of the training data. The final ensemble contains learners that are trained on examples sampled with a shared sampling distribution. The combination weights for the final weighting rule are obtained at each iteration based on the lowest training error among the views. Weights are updated in each iteration based on the lowest training error among all views at that iteration to form the shared sampling distribution used at the next iteration. In each iteration, a weak learner is selected from the pool of weak learners trained on disjoint views based on the lowest training error among all views, resulting in a lower training and generalization error bound of the final hypothesis.
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