It is a very challenging work to deal with large regression problems based on support vector machines.

基于支持向量机的大样本回归问题一直是一个非常具有挑战性的课题。

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Support vector machines (SVM) are a kind of novel machine learning methods, based on statistical learning theory, which have been developed for solving classification and regression problems.

支持向量机是一种基于统计学习理论的新颖的机器学习方法，该方法已广泛用于解决分类和回归问题。

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Least square support vector machines regression without sparsity needs longer training time currently, and is not adapted to online real-time training.

现有最小二乘支持向量机回归的训练和模型输出的计算需要较长的时间，不适合在线实时训练。

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