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.
根据支持向量的几何分布特性，提出相邻边界模型的概念以及一种支持向量预选算法。
According to the geometry distribution property of Support Vector(SV), this paper proposes the concept of adjacent boundary model and SV pre-selecting algorithm.
而支持向量机(SVM)能够在一个高维特征空间中灵活的判别边界，具有很强全局收敛性。
The Support Vector Machine (SVM) can flexible to decide boundary in a high-dimensional feature space, because of its strong global convergence.
文中提出了一种基于边界近邻的最小二乘支持向量机，采用寻找边界近邻的方法对训练样本进行修剪，以减少了支持向量的数目。
A new least squares support vector machines based on boundary nearest was proposed, which reduced the number of support vector by using boundary nearest methods pruning the training Sam.
此外，本文将支持向量机算法应用于不规则边界边值问题和多介质问题。
Moreover, we have solve boundary value problems with irregular boundary and several medias by SVM.
根据粗糙集理论的边界区域和V -支持向量机的优点对支持向量聚类算法进行改进。
According to the border region of rough set theory and the merits of V-support vector machine, the algorithm of support vector clustering is improved.
提出了一个新的支持向量机模型——基于边界调节的支持向量机，并利用拉格朗日定理得到了这种支持向量机的对偶目标函数。
In order for an SVM to be more robust to noise, a new SVM model i. e., the support vector machine based on adjustive boundary SVMAB is proposed.
提出了一个新的支持向量机模型——基于边界调节的支持向量机，并利用拉格朗日定理得到了这种支持向量机的对偶目标函数。
In order for an SVM to be more robust to noise, a new SVM model i. e., the support vector machine based on adjustive boundary SVMAB is proposed.
应用推荐