利用支持向量决策函数排序法(SVDFRM)，通过支持决策向量函数得到网络行为的特征贡献率并提取网络行为的重要特征。

A support vector decision function ranking method (SVDFRM) is used to calculate the contribution of network behaviors features, and then important network behaviors features are extracted.

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这种学习可以使用神经网络或者支持向量机，不过用决策树也可以实现类似的功能。

This sort of learning could take place with neural networks or support vector machines, but another approach is to use decision trees.

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为了提高歼击机故障诊断的准确性与实时性，提出一种基于决策树型组合策略的多重核学习支持向量机诊断方法。

Based on decision tree combined strategy and multiple kernel learning support vector machines, a new fault diagnosis method is proposed to improve the precision and speed of fighter fault diagnosis.

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