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最优超平面     
相关语句
  optimal hyperplane
     Using GA to Obtain Optimal Hyperplane of SVM
     用遗传算法求SVM的最优超平面
短句来源
     3. This part constructed fuzzy linear support vector regression when the parameters of optimal hyperplane are fuzzy (fuzzy vector or fuzzy number).
     3.当最优超平面中的参数为模糊量(模糊向量或模糊数)时,建立了模糊线性支持向量分类机和模糊线性支持向量回归机。
短句来源
     The paper proposed a new method of clustering with maximal margin based on the idea of optimal hyperplane and support vector machine.
     提出了基于最优超平面与支持向量机思想的最大间隔聚类算法。
短句来源
     Genetic algorithm is a method to search the optimal solution globally,with which a direct method for searching an optimal hyperplane is given. It is different from the quadratic programing method.
     由此,利用遗传算法得到了一个直接求最优超平面近似解的方法,该方法不同于传统的二次规划方法。
短句来源
  optimal margin hyperplane
     In this paper,linear and nonlinear SVMs are introduced based on the notion of optimal margin hyperplane,several popular training algorithms are presented,and some limitations and future research issues are also discussed.
     该文首先引入最优超平面的概念,然后对线性SVMs和非线性SVMs进行介绍,给出一些常用的训练算法,并指出SVMs存在的局限和将来可能的研究内容。
短句来源
  optimal hyper-plane
     When samples are linear non-separable, the input vectors are mapped to a high dimensional feature space using the predefined nonlinear mapping, and then one optimal hyper-plane is constructed in the high dimensional feature space.
     对于样本集线性不可分的情况,通过事先确定的非线性映射将输入矢量映射到一个高维特征空间,然后在此高维空间中构建最优超平面
短句来源
  “最优超平面”译为未确定词的双语例句
     The Classified Models of Breast Cancer Based on Optimal Super-plane Method
     基于最优超平面方法的乳房肿瘤分类模型
短句来源
     At first, basic theory of SLT including the VC dimension, the bound of extending and the principle of structural risk minimization (SRM) are presented.
     然后详细阐述了统计学习理论的基本理论,包括VC维理论、推广性的界和结构风险最小化; 接下来介绍了最优超平面及其构造;
短句来源
     A modified SVM-KNN classifier combined SVM with modified KNN is presented in the same way.
     在修正SVM-KNN算法中,在特征空间中计算样本到SVM最优超平面的距离,当该距离大于给定的阈值时使用SVM对样本进行分类,否则使用修正KNN方法。
短句来源
     Support Vector Machine is a powerful instrument which can solve the small sample study problem.
     支持向量机(SVM)是解决小样本学习问题的有力工具,其关键是如何得到判别样本类别的最优超平面
短句来源
     The SVM maps input vectors nonlinearly into a high dimensional feature space and constructs the optimum separating hyperplane to realize the signal recognition.
     SVM将特征向量非线性地映射到高维特征空间中,并建立1个最优超平面来实现信号调制方式的分类。
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  optimal hyperplane
The linear separability, fuzzy margin, optimal hyperplane, generalization and soft fuzzy margin algorithms are discussed.
      
For unweighted orthogonall1-fits essentially the same number of points are on either side of an optimal hyperplane.
      
SVC leads to the optimal hyperplane (maximal margin) criterion for separable datasets but, in the nonseparable case, the SVC minimizes the L1 norm of the training errors plus a regularizing term, to control the machine complexity.
      
Any data point xi corresponding to a nonzero ai is a support vector of the optimal hyperplane.
      
As shown in figure 3, the optimal hyperplane occurs when the margin between the classes is maximised.
      
更多          
  optimal margin hyperplane
In practice, the optimal margin hyperplane can be obtained solving a quadratic programming problem.
      
  the optimal hyperplane
SVC leads to the optimal hyperplane (maximal margin) criterion for separable datasets but, in the nonseparable case, the SVC minimizes the L1 norm of the training errors plus a regularizing term, to control the machine complexity.
      
Any data point xi corresponding to a nonzero ai is a support vector of the optimal hyperplane.
      
As shown in figure 3, the optimal hyperplane occurs when the margin between the classes is maximised.
      
Constructing the optimal hyperplane is equivalent to find all the nonzero ai.
      
Notice that this measure can be heavily affected by outliers that happen to be on the wrong side of the optimal hyperplane.
      
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