最优分类超平面原理使SVM在解决线性可分问题时有很好的表现。
The principle of finding optimized decision boundary give SVM excellent performance on linear separatable problems.
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 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.
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 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 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.
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