SVM kernel function is one of the most critical factors that affect the recognition rate.

SVM核函数是众多影响识别率因素中最明显的。

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With SVM, there is no a uniform mode to choice SVM's kernel function and its parameters.

在SVM学习中，对SVM的核函数及其参数的选择还没有形成一个统一的模式。

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In this paper, an SVM-based approach applied to predict steel quenching degree is presented, and the effects of selecting kernel function on SVM modeling are also analyzed.

本文提出了基于支持向量机模型预测钢淬透性的方法，并分析了核函数的选择对支持向量机建模的影响。

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