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。
Support vector machines is a new statistical learning method based on structural risk minimization principle, and it has integrated theory and valid learning generalization ability.
支持向量机是一种基于结构风险最小原则的新型机器学习方法，具有完备的理论依据和良好的学习泛化能力。
Support vector machine (SVM) is a novel and powerful learning method which is derived based on statistical learning theory (SLT) and the structural risk minimization principle.
建立在统计学习理论基础之上的支持向量机(SVM)，是一种基于结构风险最小的小样本机器学习方法。
For more effective meteorological data mining, this thesis introduces the quotient space granular computing theory, grey model, structural machine learning algorithm and so on.
为了更加有效地进行瓦斯数据挖掘，本文引入了商空间粒度计算理论、灰色模型、覆盖算法等。
For more effective meteorological data mining, this thesis introduces the quotient space granular computing theory, grey model, structural machine learning algorithm and so on.
为了更加有效地进行瓦斯数据挖掘，本文引入了商空间粒度计算理论、灰色模型、覆盖算法等。
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