Compared with statistical theory, statistical learning theory focuses on the machine learning of small sample size and can trade off between the complexity of models and generalization performance.
与传统统计学相比，统计学习理论是一种专门研究小样本情况下机器学习规律的理论。
Support vector machine (SVM) based on the structural risk minimization of statistical learning theory is a method of machine learning for small sample set.
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。
However, support vector machine (SVM) can better solve problem of small-sample learning and provides the foundation for solving intelligent diagnosis problems.
而支持向量机能够较好地解决小样本学习问题，为解决智能诊断的这一问题提供了基础。
As a new machine learning method, SVM can solve the small sample, nonlinear, high dimension and local minima, the actual problem.
作为一种新的机器学习方法，SV M能较好地解决小样本、非线性、高维数和局部极小点等实际问题。
As a new machine learning method, SVM can solve the small sample, nonlinear, high dimension and local minima, the actual problem.
作为一种新的机器学习方法，SV M能较好地解决小样本、非线性、高维数和局部极小点等实际问题。
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