文本提出了一种基于模糊向量空间模型和径向基函数网络的分类方法。
A classification method based on fuzzy vector space model and radial basis function network is presented in this paper.
新的目标准则函数考虑了数据集样本的模糊隶属关系和样本几何分布两个方面的因素，使算法的鲁棒性和分类的正确性大大加强。
Two factors are considered in cluster-validity criterion to enhance the robustness of algorithm and the validity of clustering, one is fuzzy membership and the other is geometric property.
针对文本自动分类问题，提出了一种基于模糊向量空间模型和径向基函数网络的分类方法。
Aimed at the problems of document automatic classification, a classification method is proposed based on fuzzy vector space model and RBF network.
针对一类基于模糊感知器的神经模糊分类器，分析了隶属函数限制条件对分类结果的影响。
For a neuro_fuzzy classifier based on the fuzzy perceptron, this paper analyses how membership function constraints affect the classification result.
使用模糊测度作为神经网络的目标函数可以有效地描述像素类别的不确定性，从而通过使其最小实现图像分类优化。
Use fuzziness measures as objective function of neural network can depict uncertainty of pixels' category validly so as to optimize image classification by minimizing the objective function.
本文提出了一种基于模式类特征空间统计分布的模糊隶属度函数模型，可有效地反映模式在特征空间中的真实分布，用于模式分类器输入特征的模糊化可获取更好的识别性能。
In this paper a model of discrete fuzzy membership function based on statistical distribution of features of pattern is presented. It is used for the fuzziness of input features of classifier.
然后用模糊聚类的方法实现目标的分类，并根据实际数据的特点灵活地选择和调整了模糊聚类的距离函数，比较简洁地实现了目标的分类。
Objects are classified by using fussy clustering method. Distance function is selected and adjusted in conformity to the character of practical data. Elliptical objects are classified concisely.
然后用模糊聚类的方法实现目标的分类，并根据实际数据的特点灵活地选择和调整了模糊聚类的距离函数，比较简洁地实现了目标的分类。
Objects are classified by using fussy clustering method. Distance function is selected and adjusted in conformity to the character of practical data. Elliptical objects are classified concisely.
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