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未标识样本分类的模糊神经网络分类器OFMM*

, PP. 173-179

Keywords: 分类器,多维度收缩(MDS),模糊最大最小神经网络(FMMN),相似性测度,未标识样本

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Abstract:

为了解决未标识样本的分类问题,提出一种基于多维度收缩的、新的排序模糊神经网络分类器模型OFMM.该模型首先利用多维度收缩法对输入的所有样本进行排序,然后获得样本间的相似性测度值.并利用该相似性测度值指导随后的分类器超盒扩张与压缩过程,从而使得该模型不仅提高对未标识样本进行有效分类的性能,而且无论是在网络结构方面,还是在训练时间方面都有所改进.有关标准数据集的实验结果表明,该模型明显优于传统的通用模糊神经网络,是一种较实用且有效的分类器.

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