【摘要】 本文在提出规范、规范满等概念的基础上,对CC4神经网络分类计算的倾向性进行了理论分析。并针对文本分类,提出了基于神经网络的增量式索引建立方法,将以词频为基础表示的高维文本信息映射到低维数据空间。为了使CC4神经网络应用到基于文本信息空间索引的分类技术中,将空间索引变换为CCA神经网络可以接受的二值向量,使得CC4神经网络以空间索引为基础,进行文档分类。最后给出了相应的实验结果。更多还原

【Abstract】 On the basis of the concept of Standardization and Standard Fullness, the computational bias of CC4 networks is analyzed theoretically. For text classification, a neural network based incremental indexing method is proposed to map the high dimensional text information represented as term frequency vectors to lower dimensional data points. To make CC4 neural networks be able to be applied in document classification with their spatial indexing, indexes are transformed into binary sequence as the input of CC4 neural networks so that CC4 can perform document classification. The sensitivities of the classification precision to the generalization radius of CC4 networks is also analyzed theoretically. Finally, experimental results are given to demonstrate the correctness of our theoretical analysis.更多还原