支撑矢量机是根据统计学习理论提出的一种新的学习方法，即使用核函数在高维空间里进行有效的计算。
The support vector machine is a novel type of learning technique, based on statistical learning theory, which USES Mercer kernels for efficiently performing computations in high dimensional Spaces.
对空间矢量脉宽调制(SVPWM)的理论进行了较详细的讨论。
The theory of space vector pulse width modulation (SVPWM) is discussed in detail.
在有限维矢量空间理论的基础上，本文对该算法的结构力学意义进行了讨论。
Its significance in structural mechanics is investigated on the basis of theory of the finite-dimension vector space.
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