Description: 用于人脸识别的模糊独立成分分析+主成分分析，用模糊支持向量机进行的分类。-Fuzzy Face Recognition for independent component analysis+ principal component analysis, using fuzzy support vector machine classification. Platform: |
Size: 2030592 |
Author:戴欢 |
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Description: ：对每一个训练点都定义点模糊度，利用其隶属函数所包含的信息量来确定模糊度，在
此基础上对传统的支持向量机算法进行了改进，提出了基于模糊支持向量机的医学图像分类
技术。-: For each training point of ambiguity points are defined using the membership function to determine the amount of information contained ambiguity, on the basis of the traditional support vector machine algorithm was proposed to improve the fuzzy support vector machine based on medical image classification techniques. Platform: |
Size: 363520 |
Author:刘东 |
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Description: 基于模糊的最小二乘支持向量机，可用于分类，效果较好。-Fuzzy least squares support vector machine can be used for classification, the effect is better. Platform: |
Size: 1024 |
Author:陈晨 |
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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: 提出一种基于模糊C 均值的支持向量机分类算法，通过模糊C 均值算法对未知类别数据
进行划分，然后再利用支持向量行对划分后的数据机进训练。解决了以往人们应用支持向量机进行
数据分类识别前必须采用已知类别的数据对支持向量机进行训练的弊端，提高了数据分类的效率。-Support vector machines classification algorithm is proposed based on Fuzzy C-Means, Fuzzy C-Means algorithm unknown class data division, and then use the support vector line modem divided into training. Solve the drawbacks of the people in the past applied Support Vector Machine data classification and recognition must be known before the category of data for training support vector machine, to improve the efficiency of data classification. Platform: |
Size: 237568 |
Author:罗朝辉 |
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Description: Fuzzy support vector machine for classification of EEG signals using wavelet-based features Platform: |
Size: 150528 |
Author:payam/hamid |
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Search - fuzzy support vector machine for classification