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。
Kernel Methods are concerned with mapping input data into a higher dimensional vector space where some classification or regression problems are easier to model.
核函数方法关心的是如何把输入数据映射到一个高维度的矢量空间，在这个空间中，某些分类或者回归问题可以较容易地解决。
Kernel Methods are concerned with mapping input data into a higher dimensional vector space where some classification or regression problems are easier to model.
核函数方法关心的是怎样把输入数据映射到一个高维度的矢量空间，在这个空间中，某些分类或者回归问题可以较容易地解决。
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。
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。
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