Even for binary, linear classification it is data dependent whether it is better to train the geometrical model (SVM?) or a probabilistic one.

即使是二进制的，线性分类它是依赖于数据是否是更好的列车的几何模型(SVM ？)或概率。

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The support vector machine (SVM) is a linear classification machine, it is used commonly in the pattern recognition and nonlinear regression.

支持向量机(SVM)是一种线性机器，广泛用于模式分类和非线性回归。

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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。

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