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。
Introducing the novel fuzzy membership model into Adaptive Support Vector Machine (ASVM), we propose an Adaptive fuzzy Support Vector Machine algorithm (AFSVM).
并将新的模糊隶属度模型引入自适应支持向量机，提出了模糊自适应支持向量机算法。
The adaptive parameter optimization algorithm is applied to dynamically search the parameters of FSVM(Fuzzy Support Vector Machine) to enhance the convergence speed.
为提升算法的收敛速度，采用参数自适应优化算法动态搜索模糊支持向量机的模型参数。
The adaptive parameter optimization algorithm is applied to dynamically search the parameters of FSVM(Fuzzy Support Vector Machine) to enhance the convergence speed.
为提升算法的收敛速度，采用参数自适应优化算法动态搜索模糊支持向量机的模型参数。
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