Bi-Fuzzy Support Vector Machine 双模糊支持向量机
Distance Fuzzy Support Vector Machine 距离模糊支持向量机
multi-class fuzzy support vector machine 模糊多类SVM
fuzzy hypersphere support vector machine 模糊超球面支持向量机
fuzzy one-class support vector machine 模糊一类支持向量机
The experiments show using fuzzy support vector machine significantly improves the overall recognition rate.
实验结果显示，采用模糊支持向量机有效地提高了识别准确度。
Emulational experimental result shows that this new fuzzy support vector machine method not only has higher classified accuracy, but also has stronger test capability for the membership degree.
仿真试验结果表明这种新的模糊支持向量机方法不但有较高的分类准确率，而且对隶属度有很强的预测能力。
In order to improve the training efficiency, an advanced Fuzzy Support Vector Machine (FSVM) algorithm based on the density clustering (DBSCAN) is proposed.
为了提高模糊支持向量机在数据集上的训练效率，提出一种改进的基于密度聚类(DBSCAN)的模糊支持向量机算法。
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