助手标题  
全文文献 工具书 数字 学术定义 翻译助手 学术趋势 更多
查询帮助
意见反馈
   self organizing map 在 计算机软件及计算机应用 分类中 的翻译结果: 查询用时:0.013秒
图标索引 在分类学科中查询
所有学科
计算机软件及计算机应用
自动化技术
武器工业与军事技术
机械工业
气象学
化学
更多类别查询

图标索引 历史查询
 

self organizing map
相关语句
  自组织映射
    It can be divided into three parts: Introduce the method of detecting outlier by Self Organizing Map on the base of comparing existing detection methods of outlier;
    在自组织映射检测结果的基础上,提出了两个互补的结合领域知识区分不同类型异常数据的方法;
短句来源
  “self organizing map”译为未确定词的双语例句
    This paper unfolds with the degree of supervision,summarizing several methods in supervised,unsupervised and semi supervised learning strategies NBC(Nave Bayes Classifier),FCM(Fuzzy C Means),SOM(Self Organizing Map),ssFCM(semi supervised Fuzzy C Means)and gSOM(guided Self Organizing Map)and also their application in text categorization.
    本文以监督的程度为线索 ,综述了分属全监督 ,非监督以及半监督学习策略的若干方法—NBC(Na veBayesClassifier) ,FCM (FuzzyC Means) ,SOM (Self OrganizingMap) ,ssFCM (semi supervisedFuzzyC Means)和gSOM(guidedSelf OrganizingMap) ,并应用于文本分类中。
短句来源
    Improving the speed of computation in image classification is one of the most elements in image dispatching,so improving the algorithm speed becomes the important aspect in study. Kmeans algorithm,FCM,self organizing map network algorithm are all the image classification methods.
    图像分类中提高分类的运算速度是图像处理的一个重要因素 ,提高运算速度成为研究的一个重要方面。
短句来源
查询“self organizing map”译词为用户自定义的双语例句

    我想查看译文中含有:的双语例句
例句
为了更好的帮助您理解掌握查询词或其译词在地道英语中的实际用法,我们为您准备了出自英文原文的大量英语例句,供您参考。
  self organizing map
In fact, a Self Organizing Map (SOM), combined with multiple recurrent neural networks (RNN) has been trained to predict the components of noisy and large data set.
      
The training set is fed to a self organizing map neural network to cluster the measurements.
      
The Self Organizing Map (SOM) algorithm has been utilized, with much success, in a variety of applications for the automatic organization of full-text document collections.
      
In this paper we study the sensitivity of the Self Organizing Map to several parameters in the context of the one-pass adaptive computation of cluster representatives over non-stationary data.
      
We only need to replace normal E-step with the modified E-step presented here to obtain a self-organizing map version of the given mixture model.
      
更多          


Clustering algorithms in feature space are important methods in image segmentation. The choice of the effective feature parameters and the construction of the clustering method are key problems encountered with clustering algorithms. In this paper, the multifrac-tal dimensions are choren as the segmentation feature parameters which are extracted from original image and wavelet-transformed image. Self-Organizing Map(SOM) network is applied to cluster the segmentation feature parameters. The...

Clustering algorithms in feature space are important methods in image segmentation. The choice of the effective feature parameters and the construction of the clustering method are key problems encountered with clustering algorithms. In this paper, the multifrac-tal dimensions are choren as the segmentation feature parameters which are extracted from original image and wavelet-transformed image. Self-Organizing Map(SOM) network is applied to cluster the segmentation feature parameters. The experiment shows that the performance of the presented algorithm is very good.

特征空间聚类分割方法存在的关键问题是有效的特征参数提取和聚类方法的构造。针对这两个问题,本文采用小波变换的多尺度分析方法提取图象的多尺度分形维数作为分割特征参数,用Kohonen自组织特征映射实现特征空间聚类,获得了良好的分割效果。

Several methods of extracting rules from database using neural networks are surveyed in this paper:extracting rules based on BP network and BP algorithm using combining weight algorithm.Extracting fuzzy rules using fuzzy inferencing neural networks and CamDelta algorithm.Extracting fuzzy control rules from data based on the competitive neural networks and DCL algorithm.Discovering rules using the growing self-organizing map neural networks and hierarchical clustering algorithm.Extracting noncertainty...

Several methods of extracting rules from database using neural networks are surveyed in this paper:extracting rules based on BP network and BP algorithm using combining weight algorithm.Extracting fuzzy rules using fuzzy inferencing neural networks and CamDelta algorithm.Extracting fuzzy control rules from data based on the competitive neural networks and DCL algorithm.Discovering rules using the growing self-organizing map neural networks and hierarchical clustering algorithm.Extracting noncertainty rules using CFNet networks with certainty factor.Discovering rules numerical-linguictic data using fuzzy granular neural networks and heuristic learning algorithm.Finally,some confronting problems for extracting rules and ideas to solve are presented.And a rule extracting methods based on multiagent in distributed environment is contructed.6figs.,17refs.

评述了利用神经网络从数据库中进行规则发现的几种方法 ,采用权值组合算法提取规则 ;利用模糊推理神经网络 ,采用CamDelta算法提取模糊规则 ;基于从数据中提取模糊控制规则 ;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 ;利用CFNet网络 ,基于可信度因子 ,提取不确定性规则 ;利用模糊颗粒神经网络 ,采用启发式学习算法 ,从数值 -语言数据中发现规则 提出了数据库中提取规则所面临的几个问题 ,以及解决这些问题的某些思路 具体提出了一种分布式环境下基于多Agent技术的规则提取方法 图 6,参 17

An efficient target recognition method for large scale data is proposed in this paper,which is based on self-organizing map (SOM) neural network and support vector machines (SVMs).The target data set is divided into clusters by SOM first.Then,the support vector machines are applied to classify targets.The new method is used to classify the complex XOR problem,Iris and Appendicitis data,and the experimental results show that the new method can obtain better recognition results for the complex...

An efficient target recognition method for large scale data is proposed in this paper,which is based on self-organizing map (SOM) neural network and support vector machines (SVMs).The target data set is divided into clusters by SOM first.Then,the support vector machines are applied to classify targets.The new method is used to classify the complex XOR problem,Iris and Appendicitis data,and the experimental results show that the new method can obtain better recognition results for the complex pattern classification of large scale data,and the trainning time is shorter than that by using the support vector machine method only.

本文提出了一种基于自组织特征映射神经网络 (SOM)和支撑矢量机 (SVM)相结合的复杂模式的大规模数据的分类方法 .该方法首先利用自组织特征映射神经网络对待识目标进行聚类 ,然后应用支撑矢量机方法对其进行分类识别 .通过对复杂异或 (XOR)分类问题 ,以及实际的Iris和Appendicitis数据分类问题等的分类实验 ,且与仅用支撑矢量机的分类方法比较 ,结果表明 ,本文提出的方法对复杂模式的大规模数据的分类识别问题具有较好的效果 ,且训练时间大幅度减小 .

 
<< 更多相关文摘    
图标索引 相关查询

 


 
CNKI小工具
在英文学术搜索中查有关self organizing map的内容
在知识搜索中查有关self organizing map的内容
在数字搜索中查有关self organizing map的内容
在概念知识元中查有关self organizing map的内容
在学术趋势中查有关self organizing map的内容
 
 

CNKI主页设CNKI翻译助手为主页 | 收藏CNKI翻译助手 | 广告服务 | 英文学术搜索
版权图标  2008 CNKI-中国知网
京ICP证040431号 互联网出版许可证 新出网证(京)字008号
北京市公安局海淀分局 备案号:110 1081725
版权图标 2008中国知网(cnki) 中国学术期刊(光盘版)电子杂志社