This paper propose a refined self-organizing map that can directly handle categorical data or hybrid data, map the data to lower dimensions, and also uncover the similarity among data.
本研究针对此问题，提出改良式自组映射图，能直接处理种类型态或混合型态的资料，同时在投射后的低维度空间，反映高维度资料之间的相似度。
Self Organizing Map is a method of artificial neural network, which implements pattern recognition and data clustering simultaneously.
自组织特征映射是一种人工神经网络方法，可以同时实现模式识别和数据分类。
Classical feature extraction methods include: Principle Component Analysis, Singular Value Decomposition, Projection Pursuit, Self-Organizing Map, and so on.
传统的特征提取方法主要有：主分量分析、奇异值分解、投影追踪、自组织映射等。
This paper tries to make some improvements on applying Self-Organizing-Map (SOM) to automatic clustering of Chinese nouns, so as to generate a better Chinese semantic map.
本文试图对自组织映射神经网络（SOM）应用于汉语名词语义自动聚类做某些改进。
This paper tries to make some improvements on applying Self-Organizing-Map (SOM) to automatic clustering of Chinese nouns, so as to generate a better Chinese semantic map.
本文试图对自组织映射神经网络（SOM）应用于汉语名词语义自动聚类做某些改进。
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