High-dimensional Feature Space

The Support Vector Machine (SVM) can flexible to decide boundary in a high-dimensional feature space, because of its strong global convergence.

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。

Feature space is high dimensional and sparse in text categorization, the process of dimension reduction is a very key problem for large-scale text categorization.

文本分类中特征向量空间是高维和稀疏的，降维处理是分类的关键步骤。

The received mixing signals are first mapped to high-dimensional kernel feature space, and a feature vector basis given by the fitness function of the kernel feature space is constructed.

所接收的混合信号首先被映射到高维的内核特征空间，和由内核特征空间上的适应度函数给出的特征矢量的基础构造。