自组织映射

自组织映射神经网络（SOM）在客户分类中的一种应用

An Application of SOM Neural Network in Customer Classification

AffinityPropagation算法能够取得较优的结果，自组织映射算法在运行时间上更有优势。

Affinity Propagation algorithm can achieve the best precision in our experiment , and the SOM algorithm is superior in the real running time .

一种基于自组织映射神经网络的Web页面个性化推荐模型

An individualized web page recommendation model based on self-organizing map

自组织映射在Web结构挖掘中的应用

The Usage of Self-Organizing Map in Web Structure Mining

提出基于自组织映射（SOM）和SVM结合的电力负荷短期预测方法。

Short-term load forecasting based on SOM and SVM is proposed .

讨论了自组织映射网络（self-organizingmap，SOM）的发展与应用进展情况。

The developments and applications related to Self - Organizing Map ( SOM ) networks are reviewed .

利用自组织映射SOM实现电力系统暂态稳定评估结果可视化

SOM based visualization of power system transient stability assessment results

改进的自组织映射（SOM）蛋白质折叠算法和计算实现

Improved self-organizing map algorithm for protein folding and its realization

多级自组织映射用于心电信号QRS波群聚类

Multilayer self-organizing maps method for cluster electrocardiogram QRS wave clustering

自组织映射（SOM）聚类算法的研究

Research of Clustering Algorithm of Self-Organizing Maps Neural Networks

利用生长自组织映射神经网络，采用分级聚类SOM算法发现规则；

Discovering rules using the growing self-organizing map neural networks and hierarchical clustering algorithm .

文章讨论了自组织映射、K平均值聚类和一种有效性测度Silhouette指数。

The paper discusses self-organizing feature map ( SOM ), K-means clustering and a cluster validity measure , Silhouette index .

对IRIS数据集和入侵检测报警数据的聚类也证明了核自组织映射聚类方法的良好性能。

Examples of clustering IRIS data and alerts in intrusion detection also proved the good performance of the KSOM method .

由Kohonen提出的自组织映射神经网络（SOM）被广泛用在信息科学领域。

The SOM neural networks proposed by Kohonen have been widely applied in information sciences .

本论文提出了一种基于超立方体和超网格自组织映射（SOM）编码的多类目标识别方法。

We propose a multicategory classification method based on hypercube / hypergrid self-organization mapping ( SOM ) scheme .

在基于脑电（EEG）的脑-机接口技术中，使用可生长自组织映射（SOM）神经网络进行了5类意识任务分类的研究。

The growing self-organizing map ( Growing SOM ) was adopted to perform mental tasks classification in EEG-based brain-computer interface ( BCI ) .

提出一种使用生长、分级的自组织映射（growinghierarchicalself-organizingmap，GHSOM）模型进行基于EEG信号的意识任务分类来实现脑机接口技术的方法。

The growing hierarchical self-organizing map ( GHSOM ) model was proposed to apply to performing mental tasks classification in EEG for Brain-Computer Interface .

本文的第二个数据挖掘任务是基于自组织映射（SOM，Self-organizingmap）神经网络进行低压终端用户的负荷曲线聚类研究。

The second data mining task was to study on load profile clustering of low-voltage terminal customers based on Self-Organizing Map ( SOM ) neural networks .

自组织映射（Self-OrganizingMaps，SOM）算法是一种无导师学习方法，具有良好的自组织、可视化等特性，已经得到了广泛的应用和研究。

The self-organizing maps ( SOM ) is an unsupervised learning algorithm , which is capable of self organization and visualization and has been used in many areas .

基于RFM分析的银行信用卡客户的行为评分模型&应用自组织映射神经网络SOM和Apriori方法

The Behavioral Scoring Model of Credit Card Customers in a Bank Based on RFM & the Application of SOM and Apriori

针对基于自组织映射（SOM）的后非线性ICA方法的缺点，提出了一种新的具有全局拓扑保持特性的SOM网络权值初始化方法。

According to the drawbacks of the post-nonlinear ICA method based on SOM , an initialization method with global topology preservation property for SOM network is proposed .

根据这一储层特点，用前向（BP）网络建立储层参数的仿真计算，然后用自组织映射特征（SOM）网络来预测油层类别。

Two kinds of neural networks forward network ( BP ) and self organizing feature mapping network ( SOM ) were used in reservoir parameter simulated calculation and horizon type prediction respectively .

利用灰关联分析可以体现样本向量中各分量重要性的特性，对生长、分级的自组织映射（growinghierarchicalself-organizingmap，GHSOM）网络进行了改进。

Taking advantage of the feature of gray relation analysis which can extract the importance of each branch in sample vectors , an improved growing hierarchical self-organizing map algorithm is proposed .

本文对比研究了三种聚类算法，即K-means、自组织映射和AffinityPropagation算法，以及两种合并策略（DCM和Cosine）。

Three clustering algorithms , namely K-means , Self-Organizing Map ( SOM ) and Affinity Propagation algorithm and two combined strategies ( DCM and Cosine ) are compared and analyzed in the thesis .

根据隐含语义索引（LSI）理论和动态自组织映射神经网络理论，提出了一种文本聚类的新方法。

This paper presents a new method of text clustering by using the latent semantic index ( LSI ) and self-organizing neural network ( SNN ) .

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 .

基于Kohonen神经网络能够保持拓扑结构的自组织映射的特性，对散乱数据点进行曲面重构，建立了基于自组织特征映射神经网络的矩形网格重构模型。

Basing on Kohonen neural network 's self-organizing feature map of being capable of keeping topologic structure , surface is reconstructed from scattered data points .

为了提高金融时间序列预测可视化的效果，在经典的自组织映射SOM（Self-OrganizationMap）中引入了核（Ker-nel）的思想得到Kernel-SOM。

To improve the visualization performance of financial time series prediction , Kernel-SOM is proposed by importing Kernel into the traditional SOM ( Self-Organization Map ) .

提出了用自组织映射（SOM）网络对生物信息学中基因表达数据进行聚类分析的方法。

Method of analyzing gene expression data based on SOM networks are discussed . An SOM network is briefly introduced and its cluster characteristics and visualization methods are illustrated using simulation .

本文提出了用Kohonen自组织映射神经网络进行中文信息的概念联想。

In this paper , a neural network approach to constructing concept association of Chinese information is shown . It is based on the Kohonen SOM .