The support vector approach learns a parsimonious regression model from the given data to avoid the data over-fitting problem.

支持矢量回归方法可以在给定的资料中生成一个简洁的回归模式，以避免常规机器学习法中的资料过度学习问题。

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Similarly, with machine learning algorithms, a common problem is over-fitting the data and essentially memorizing the training set rather than learning a more general classification technique.

同样，对于机器学习算法，一个通常的问题是过适合(原文为over - fitting，译者注)数据，以及主要记忆训练集，而不是学习过多的一般分类技术。

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The result of practical application indicates that the performance of SVM has superiority over ANN and can overcome the problem of "over fitting" excellently.

实际数据处理结果表明，该方法在小样本情况下性能优于神经网络，可以很好地克服过学习问题。

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