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。
Support vector machine constructs an optimal hyperplane utilizing a small set of vectors near boundary.
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。
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.
通过采集风机样本进行SVM训练，拟合出具有最小结构风险的最优分类面，用于实时监测变压器风机的运转状态。
By collecting samples for SVM training, the optimal separating surface with the minimization structural risk is developed for real-time monitoring of the functioning of the state transformer fan.
通过采集风机样本进行SVM训练，拟合出具有最小结构风险的最优分类面，用于实时监测变压器风机的运转状态。
By collecting samples for SVM training, the optimal separating surface with the minimization structural risk is developed for real-time monitoring of the functioning of the state transformer fan.
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