separating hyperplane meaning in Chinese
分离超平面
Examples
- The separating hyperplane of traditional support vector machines is sensitive to noises and outliers
摘要传统的支持向量机分类超平面对噪声和野值非常敏感。 - Svm maps input vectors nonlinearly into a high dimensional feature space and constructs the optimum separating hyperplane in the spade to realize modulation recognition
支撑矢量机把各个识别特征映射到一个高维空间，并在高维空间中构造最优识别超平面分类数据，实现通信信号的调制识别。 - For this problem , a separating hyperplane is designed with the principle of maximizing the distance between two class centers , and a novel support vector machine , called maximal class - center margin support vector machine ( mccm - svm ) is designed
为了解决这个问题，本文以两个类中心距离最大为准则建立分类超平面，构造一个新的支持向量机，称作类中心最大间隔支持向量机。 - The idea is proposed that those increased date , which near the separating hyperplane , is significant for the forming of the new hyperplane , whenever these date are classed by the former hyperplane to test error set berr or test right set bok
与传统的增量学习方法不同，本文中，作者认为那些在分类面边缘增加的数据对分类面的改变都起着重要的作用，无论这些数据被初硕士论文支持向量机在图像处理应用中若干问题研究始分类器p划分到测试错误集berr或者测试正确集b 。 - By mapping input data into a high dimensional characteristic space in which an optimal separating hyperplane is built , svm presents a lot of advantages for resolving the small samples , nonlinear and high dimensional pattern recognition , as well as other machine - learning problems such as function fitting
Svm的基本思想是通过非线性变换将输入空间变换到一个高维空间，然后在这个新的空间中求取最优分类超平面。它在解决小样本、非线性及高维模式识别问题中表现出许多特有的优势，并能够推广应用到函数拟合等其他机器学习问题中。