基于统计学习理论中结构风险最小化原则的支持向量机是易于小样本的机器学习方法。

Support vector machine (SVM) based on the structural risk minimization of statistical learning theory is a method of machine learning for small sample set.

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针对这一缺陷，将基于小样本理论的支持向量机学习方法应用到发动机的故障诊断中。

To solve the problem of lack of fault engine sample, support vector machines, which is a method based on small sample theory is applied.

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神经网络的理论基础是最小化经验误差，这种基于传统的渐进理论的学习方法，在训练样本点无穷多时是适用的。

Because neural network is based upon empirical risk minimization and asymptotic theories, it is suitable to deal with situations where the amount of samples is tremendous and even infinite.

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