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。
Towards the end, I have again given some examples from public life and tried to map it with the Agile and self-organizing concept.
快到结尾时，我会再次从公众生活中给出一些例子，并将其映射到敏捷和自组织的概念之上。
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。
Then a classified frequency sensitive self-organizing feature map(CFSSOM) was proposed for the codebook training.
并对SOM算法进行了改进，提出了一种分类频率敏感自组织特征映射（CFSSOM）算法。
In this paper, we propose a model-based, self organizing feature map algorithm for the clustering of variable-length sequences.
本文提出一种基于模型的、适合变长符号序列的自组织聚类算法。
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。
Self organizing feature map (SOM) network can extract the internal features of parameter by self organizing and reflect them on the classified map. It can be used in problems of pattern recognition.
自组织特征映射(SOM)神经网络能通过自组织有效地提取出各特征参数间的内在特征并映射到分类模板上，它可以用于各种模式识别问题。
The paper briefly introduces the fundamentals of neural network of self-organizing feature map and on the basis of which discusses the classification of land reclamation conditions.
本文简要介绍了自组织特征映射神经网络的基本原理，并利用其原理对土地复垦的条件分类进行了初步研究。
In this paper, the basic principle of the clustering algorithm based on self-organizing feature map network is discussed, and pointed out its defects.
本文讨论了基于自组织特征映射网络聚类算法的基本原理，并指出了算法的缺陷。
The combination of curvilinear component analysis (CCA) and self-organizing feature map (SOFM) were applied to a diagnosis for fault feature extraction of bearing.
提出曲元分析(CCA)和自组织特征映射(SOFM)相结合的方法用于轴承的故障诊断特征提取。
Aiming to some flaw, people bring forward to use Self-Organizing Feature Map on collecting data to make clustering and watching at first, and obtain principium information about some collection data.
针对这些缺点提出先利用自组织映射的方法对采集的数据进行聚类和可视化，获得一些关于采集到的数据的初步信息。
The classification of simple and complex objects is investigated using the multiple layer forward neural network and the self-organizing feature map network.
本文应用多层前馈神经网络和自组织特征映射神经网络分别对简单目标和复杂飞机目标进行了分类识别。
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。
The first level of our system employs the self-organizing feature map (SOFM) to map colors of image on a two dimensional feature map.
第一层结构使用自组织特征映射神经网络（SOFM）将像素映射到二维的平面上。
The self-organizing feature map (SOFM) uses weight of network to present structure of the input data and has preferable ability of classification.
自组织特征映射（SOFM）网络利用神经元权值向量表示输入数据的结构、具有较好的分类能力。
The self-organizing feature map (SOFM) uses weight of network to present structure of the input data and has preferable ability of classification.
自组织特征映射（SOFM）网络利用神经元权值向量表示输入数据的结构、具有较好的分类能力。
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