支持向量机中遗传模糊C-均值的样本预选取方法
Pre-selection Sample Method of Genetic Algorithm Fuzzy C-Mean in Support Vector Machines
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摘要: 提出了在支持向量机(support vector machine,SVM)方法中采用遗传模糊C均值(FCM)进行样本预选取的方法,旨在保留最优分类超平面附近的样本点,去除远处样本点,使训练样本集减小,消除冗余,从而减小所需内存。并以航空影像中的居民地为例进行分析,结果表明,按比例减少样本集后的分割结果与用原样本集的基本一样。Abstract: This paper proposes the pre-selection sample method of Genetic Algorithm fuzzy C mean. The result is that hold the samples nearing the supper plane, delete the samples far off the supper plane, decrease the training set and the storage. Experiences are based on Residence, and according to reducing samples of original. The decision of reducing number of samples according to SVM's numbers of iterative and SV. The variety of iterative and SV numbers should be little while both segmentation results of reducing and original samples are almost same. This method could be extended to other kind of objects.