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.
未来的研究可以从模型函数形式的选优、特征变量的量化以及模型超样本特性的研究等方面着手，进行更多的实证研究。
It can carry on more experiential and optimizing of model form, the quantify of characteristic variables and research on the model characteristics of super-sample etc.
在确定的参数隶属函数基础上，推导了岩土样本力学参数模糊统计特征值的计算公式。
According to the membership function of parameters, the equations of fuzzy eigenvalue of sample parameters was deduced.
对于在特征空间中寻找特征模式，一般是通过假设分布函数一次性对样本空间进行分离的方法去试图获得特征空间的样本总体分布规律。
In order to find out the feature patterns from multi-dimension space, the conventional approach is to separate feature space by assuming the distributed functions of all features in one time.
对于在特征空间中寻找特征模式，一般是通过假设分布函数一次性对样本空间进行分离的方法去试图获得特征空间的样本总体分布规律。
In order to find out the feature patterns from multi-dimension space, the conventional approach is to separate feature space by assuming the distributed functions of all features in one time.
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