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。

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.

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利用强束缚量子点模型，忽略杂质对于电子波函数的影响，我们还讨论了如何利用核自旋构造量子位。

By making use of the strong bound quantum dot model and neglecting the effects of impurity on electron wave function, this thesis is also reported how to use the spin of nuclear as the quantum bit.

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核函数是SVM的关键技术，核函数的选择将影响着支持向量机的学习能力和泛化能力。

Kernel function is the key technology of SVM, the choice of kernel will affect the learning ability and generalization ability of SVM.

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