而支持向量机(SVM)能够在一个高维特征空间中灵活的判别边界，具有很强全局收敛性。
The Support Vector Machine (SVM) can flexible to decide boundary in a high-dimensional feature space, because of its strong global convergence.
大容量多媒体数据库的基于内容相似性的检索本质上是高维特征空间中一定距离函数的K近邻问题。
Searches based on content similarities in large multimedia libraries are essentially K nearest neighbor searches in high dimensional Spaces.
该方法通过计算齿轮振动信号原始特征空间的内积核函数来实现原始特征空间到高维特征空间的非线性映射。
In this approach, the integral operator kernel functions is used to realize the nonlinear map from the raw feature space of gear vibration signals to high dimensional feature space.
而SVM(支持向量机)引进核函数隐含的映射把低维特征空间中的样本数据映射到高维特征空间来实现分类。
The SVM (Support vector Machine) classifies the data by mapping the vector from low-dimensional space to high-dimensional space using kernel function.
而SVM(支持向量机)引进核函数隐含的映射把低维特征空间中的样本数据映射到高维特征空间来实现分类。
The SVM (Support vector Machine) classifies the data by mapping the vector from low-dimensional space to high-dimensional space using kernel function.
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