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To handle problems created by large data sets, the authors propose a method that uses a decision tree to decompose a data space and trains SVMs on the decomposed regions. Although there are other means of decomposing a data space, they show that the decision tree has several merits for large-scale SVM training. First, it can classify some data points by its own means, thereby reducing the cost of SVM training applied to the remaining data points. Second, it is efficient for seeking the parameter values that maximize the validation accuracy, which helps maintain good test accuracy. Third, they can provide a generalization error bound for the classifier derived by the tree decomposition method.
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