Description: 模糊支持向量机与主成分分析在人脸识别中的应用-Fuzzy Support Vector Machine with Principal Component Analysis Applied on Face Recognition Platform: |
Size: 5130240 |
Author:戴欢 |
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Description: 模糊支持向量机向量机的国外文章，可得到更精确的仿真图形-Fuzzy Support Vector Machine SVM foreign article can be a more accurate simulation graphics Platform: |
Size: 790528 |
Author:苏书惠 |
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Description: 支持向量机自动提取模糊规则，仿真结果验证了其可靠性-The use of support vector machine automatically extract fuzzy rules, simulation results verify the reliability of the method Platform: |
Size: 3072 |
Author:邓延丽 |
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Description: 基于神经网络的遥感图像分类取得了较好的效果,但存在固有的过学习、易陷入局部极小等缺点.支持向量机机器学习方法,根据结构风险最小化(SRM)原理,表现出很多优于其他传统方法的性能,本研究的基于多类支持向量机分类器的遥感图像分类取得了达95.4 的分类精度.但由于遥感图像分类类别多,所需训练样本较大,人工选择效率较低,为此提出以人工选择初始聚类质心、C均值模糊聚类算法自动标注训练样本的基于多类支持向量机的半监督式遥感图像分类方法,期望能在获得适用的分类精度的基础上有效提高分类效率-Neural net based remote sensing image classification has obtained good results. But neural net has inherent
flaws such as overfitting and local minimums. Support vector machine (SVM), which is based on Structural Risk Min-
imization(SRM), has shown much better performance than most other existing machine learning methods. Using mul-
ti-class SVM classifier high class rate of 95.4 is obtained. But for the class number of remote sensing image is much
great, manually obtaining of training samples is a much time-consuming work. So a multi-class SVM based semi-super-
vised approach is presented. It is choosed that the initial clustering centroids manually first, then label the samples as
the training ones automatically with fuzzy clustering algorithm. It is believed that this method will upgrade the classifi-
cation efficiency greatly with practicable class rate Platform: |
Size: 25600 |
Author:cissy |
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Description: 遥感影像的英文论文，支持向量机，模糊C均值，挺有用的-Remote sensing image of English papers, support vector machine (SVM), a fuzzy c-means, very useful Platform: |
Size: 9869312 |
Author:李琴 |
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Search - Fuzzy Support Vector Machine SVM