In accordance with the features of non-linear and time varying for ferment process, a support vector machines (SVM) model is established for estimating the concentration of product.

针对非线性时变的发酵过程，建立了用于产物浓度预估的支持向量机(SVM)模型。

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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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So one should prefer non-linear models like SVM with kernel or tree based classifiers that bake in higher-order interaction features.

因此，每个人都应该选择适合高阶交互特征的带核SVM或基于树的分类器。

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