This paper proposes a weighted C-SVM algorithm and analyzes its classification performance theoretically.
提出了一种加权C—SVM分类算法，并从理论上分析了算法的性能。
Thirdly, a GPU based massively data parallel C-SVM classification (GMP-CSVC) algorithm is presented to reduce the training time of SVM.
第三，针对支持向量机算法复杂度较高，难以应用于大样本分类的问题，提出了GMP-CSVC算法。
This paper researches the parameters (kernel, penalty parameter c) of SVM and the dimension of feature, which influence aerial image segmentation and classification.
研究了支持向量机参数(核函数、惩罚因子c)和影像特征维数对航空影像分割与分类的影响。
The two classical SVM algorithms-C-SVC algorithm and S-SVC algorithm have been done more research and the two algorithms performance has been compared by using practice data.
分类部分，论文在理论上分析了文本分类采用支持向量机技术的优点，对两种具体的SVM算法-C-SVC和V-SVC进行了研究并利用实例进行验证。
The two classical SVM algorithms-C-SVC algorithm and S-SVC algorithm have been done more research and the two algorithms performance has been compared by using practice data.
分类部分，论文在理论上分析了文本分类采用支持向量机技术的优点，对两种具体的SVM算法-C-SVC和V-SVC进行了研究并利用实例进行验证。
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