为了使相似性衡量尺度与样本特征的分布特点相适应，提出利用相似度分割特征集的混合核函数构造方法。
In order to adjust the similarity metrics to the distribution of the feature, a kernel function construction based on feature set division is proposed.
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.
该方法不仅考虑了样本点到类中心的距离，而且还考虑了样本间的密切度，结合这两种思想在特征空间中构造了一种新的基于动态核函数的模糊隶属度。
The fuzzy membership is defined not merely by the distance between a point and its class center, but also by two different points of the sample, which is depicted as the affinity between them.
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