Divide-and-conquer principle is a fashionable strategy to handle large-scale classification ***,many works have revealed that generalization ability is decreased by partitioning training set in most cases,because part...
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Divide-and-conquer principle is a fashionable strategy to handle large-scale classification ***,many works have revealed that generalization ability is decreased by partitioning training set in most cases,because partitioning training set can lead to losing classification *** to handle this problem,an ensemblelearning algorithm was *** used many sets of parallel hyperplanes to partition training set on which each base classifier was trained by the SVM modular network algorithm and all these base classifiers were combined by majority voting strategy when *** experimental results on 4 classification problems illustrate that ensemblelearning can effectively reduce the descent of generalization ability for the reason of increasing classifier's diversity.
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