Support vector machine constructs an optimal hyperplane utilizing a small set of vectors near boundary.
支持向量机利用接近边界的少数向量来构造一个最优分类面。
A method based on fuzzy theory is applied to explain the classification of SVM and its optimal hyperplane. An expression of fuzzy membership on doubtful classification area is listed.
应用模糊理论的方法对支持向量机分类及最优分类面进行了解释，对可疑分类区列出了模糊隶属度的表达式。
The former attempts to find an optimal hyperplane that maximize margin between two classes, and the later are designed to provide an explanation of the classification using logical rules.
前者寻求最大化两类间隔的最优分类超平面，后者用逻辑规则解释分类。
The fuzzy membership of each sample is defined by affinity among samples, and by the training determine a threshold, noises and outliers are removed, which influence optimal separating hyperplane.
应用基于样本之间的紧密度确定每个样本的模糊隶属度，通过训练确定阀值，去除影响得到最优分类超平面的噪声和野点。
The idea of this algorithm is to find the optimal solution in the feasible region by an iterative step from one basic standard hyperplane to another.
此算法的基本思想是在规划问题的可行域中由所建的一个切割面到另一个切割面的不断推进来求取最优的。
The idea of this algorithm is to find the optimal solution in the feasible region by an iterative step from one basic standard hyperplane to another.
此算法的基本思想是在规划问题的可行域中由所建的一个切割面到另一个切割面的不断推进来求取最优的。
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