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。
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。
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。
The fuzzy judgment support vector machine algorithm based on loss function is proposed, and compared with fuzzy sample support vector machine algorithm.
提出了基于损失函数的模糊判决支持向量机算法，并与模糊样本支持向量机算法进行了比较。
It is proposed a fuzzy forecast control method based on support vector machine. The applications of the machine to the train start-up control are given.
提出支持向量机-模糊预测控制方法，介绍支持向量机在列车启动控制过程中的应用。
The trapezoidal fuzzy number sample is one of non-real random samples. This dissertation will discuss the support vector machine base on trapezoidal fuzzy numbers.
梯形模糊数样本是一类非随机样本，本文将讨论基于梯形模糊数样本的支持向量机。
In order to improve the segmentation performance of images corrupted by impulse noise and Gaussian noise, fuzzy weighted support vector machine is proposed.
针对图像在获取和传输过程中易受各种噪声污染的事实，为了提高支持向量机对噪声图像的分割性能，提出了模糊权重支持向量机。
The fuzzy membership based on the affinity among samples for support vector machine effectively distinguishes between support vectors and outliers or noises.
将其应用于模糊支持向量机方法中，较好地将支持向量与含噪声或野值样本区分开。
The fuzzy membership based on the affinity among samples for support vector machine effectively distinguishes between support vectors and outliers or noises.
将其应用于模糊支持向量机方法中，较好地将支持向量与含噪声或野值样本区分开。
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