Support vector machine is a kind of machine study algorithm based on statistic theory, it has special advantage in solving small sample, non-linear and high dimension mode recognition.
支持向量机是一种基于统计理论的机器学习算法，在解决小样本、非线性及高维模式识别中有独特的优势。
Chaos and support vector machine theory has opened up a new route to study complicated and changeable non-linear hydrology time series.
混沌和支持向量机理论为研究复杂多变的非线性水文时间序列开辟了新的途径。
Experimental results indicate that the support vector machine performs a number of unique advantages in solving the small sample size, non-linear problems.
经过实验证明支持向量机在解决小样本、非线性问题中表现出很好的优势。
For non-linear problem, the forecasting technique of pre-classification and later regression was proposed, based on the classification approach of Support Vector Machine (SVM).
针对非线性问题，提出了基于支持向量机分类基础的先分类、再回归的预测方法。
The support vector machine possesses the ability to solve problems such as small sample, non-linear, and the directed acyclic graphs algorithm has the advantage of high classification accuracy.
支持向量机具有较好的解决小样本、非线性问题的能力，而DAG算法具有分类精度高的优点。
The support vector machine possesses the ability to solve problems such as small sample, non-linear, and the directed acyclic graphs algorithm has the advantage of high classification accuracy.
支持向量机具有较好的解决小样本、非线性问题的能力，而DAG算法具有分类精度高的优点。
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