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geo knowledge
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  “geo knowledge”译为未确定词的双语例句
     Based on the FUZZY ISODATA method, the article discusses the improving algorithm in which the supervised samples with priori geo knowledge and random samples can be fused. So the new method is called Fuzzy clustering algorithms with partial supervision.
     在Fuzzy-ISODATA 方法的基础上,探讨如何在样本数据集中融合部分知识和随机样本,通过聚类分析获得目标类别的模糊隶属度矩阵和特征空间的特征模式的方法。
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  相似匹配句对
     Acquisition and Application of Multi-Source Landuse Geo-Knowledge
     多源土地利用地学知识的获取与应用
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     (f,) -genetic knowledge;
     给出 (f, f) 遗传知识 ;
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     On Implication of knowledge
     略论知识的含义
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     Geo-ontology
     地理信息本体论
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     On Geo-economics
     浅析地缘经济学
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  geo knowledge
We describe this as a flow from geo-information dead-ends through to a point where geo-knowledge is easily available, retrievable and re-usable.
      
Therefore, fully automating the geoquery and geo-assembly steps of geo-knowledge process is quite possible in the near term.
      
In the geo-knowledge discovery process, the geo-data and information assembly services involve in data reduction and transformation services.
      


More and more interest has been paid on Geo\|data mining and knowledge discovery from large database with the rapid growth of Geo\|data volume and eagerness for the Geo\|knowledge. This paper presents a data\|set partition model based on information entropy and mutual information. The author argued that the largest information entropy deduction is in accordance with the significant Geo\|data pattern. With this kernel theoretical base, information\|entropy\|based decision\|tree...

More and more interest has been paid on Geo\|data mining and knowledge discovery from large database with the rapid growth of Geo\|data volume and eagerness for the Geo\|knowledge. This paper presents a data\|set partition model based on information entropy and mutual information. The author argued that the largest information entropy deduction is in accordance with the significant Geo\|data pattern. With this kernel theoretical base, information\|entropy\|based decision\|tree model and spatial\|temopral clustering by partition model were developed.

从信息熵的基本概念出发,认为地学空间数据子集划分产生的互信息或熵减源于子集划分,使得各个子集的不确定性或模糊性降低,并且子集之间的差异性增大。因此具有最大熵减的子集划分方案代表一定的地学模式和地学规律。以此为基础分别探讨了地学数据属性要素的子集划分产生多维属性关联规则,以及通过空间和时间的子集分割来进行聚类的方法

Remote sensing information is the synthetical reflection of the earth surface with definite scale space.Since the earth system is characterized by complexity and opening, the information from the earth surface also should hold the characteristics of multi dimension and infiniteness.Additionally, in the process of information transfer by remote sensing imaging system, the information is somehow attenuated or amplified, and there exists complicated correlativities among the units of remote sensing data and features.All...

Remote sensing information is the synthetical reflection of the earth surface with definite scale space.Since the earth system is characterized by complexity and opening, the information from the earth surface also should hold the characteristics of multi dimension and infiniteness.Additionally, in the process of information transfer by remote sensing imaging system, the information is somehow attenuated or amplified, and there exists complicated correlativities among the units of remote sensing data and features.All of above reasons determine that the remote sensing geo processing and geo analysis always have uncertainty and multi solutions.Thus, fuzzy classification is one of the most important trend of research of remote sensing image classification.Based on the FUZZY ISODATA method, the article discusses the improving algorithm in which the supervised samples with priori geo knowledge and random samples can be fused.So the new method is called Fuzzy clustering algorithms with partial supervision.Finally, the method is also successfully applied in a case of land cover classification in Yuanlang plain, Hong Kong.The result shows that great improvement is arrived in precision and flexibility comparing to the ISODATA method.

遥感信息主要反映的是地球表层信息。由于地球表层系统的复杂性和开放性,地表信息是多维的、无限的,遥感信息传递过程中的局限性以及遥感信息之间的复杂相关性,决定了遥感信息其结果的不确定性和多解性。模糊分类是遥感影像分类研究的重要趋势。在Fuzzy-ISODATA 方法的基础上,探讨如何在样本数据集中融合部分知识和随机样本,通过聚类分析获得目标类别的模糊隶属度矩阵和特征空间的特征模式的方法。提出了基于该方法的遥感影像模糊分类模型

In recent years the artificial neural network has been developed and applied to remotely sensed data classification problem. Most modal of them are error back propagation(BP), BP learning algorithm based multi layer perceptron. Compared to the conventional statistical classifier, BPNN RS image classifier are non parametric and may have the capacity of more robust proximity especially when distributions are strongly non Gaussian, but its main shortcoming is its slow training speed, local minimum and even...

In recent years the artificial neural network has been developed and applied to remotely sensed data classification problem. Most modal of them are error back propagation(BP), BP learning algorithm based multi layer perceptron. Compared to the conventional statistical classifier, BPNN RS image classifier are non parametric and may have the capacity of more robust proximity especially when distributions are strongly non Gaussian, but its main shortcoming is its slow training speed, local minimum and even being unable to converge. The Radial Basis Functions Neural Network (RBFNN) modal, integrating the parametric statistic distribution modal and non parametric single layer perceptron modal, trains faster and more stable than BPNN while keeping the complicated proximity. In this article, the survey and analysis of the RBFNN for the classification of remotely sensed multi spectral image is presented, and the RBF RS image classification modal, detailed algorithms and realization procedures is intially raised. The framework which fuses Geo Knowledge into RBFNN by RBF functions and hierarchical clustering means with optimization evolution theory also are introduced. Finally, the case of practical application of remote sensing land cover classification in Hong Kong region is presented. After the procedure of RBFNN and BPNN approaches are synthetically analyzed, experimental results show that RBFNN approach has more advantages in train time, network structure, knowledge fusion, etc.

与传统统计方法的分类器相比较 ,人工神经网络 (ANN )方法应用于遥感影像分类 ,不需预先假设样本空间的参数化统计分布 ,具有复杂的映射能力 .大多数 AN N分类器采用误差反向传播 (BP)学习算法的多层感知器模型(BPNN) ,其主要缺陷是学习速度缓慢、容易陷入局部极小而导致难以收敛等 .基于径向基函数 (RBF)映射理论的神经网络模型融合了参数化统计分布模型和非参数化线性感知器映射模型的优点 ,在实现快速学习的同时 ,保持了高度复杂的映射能力 .该文主要探讨 RBF映射理论在遥感影像分类中的具体算法和实现过程 ,并初步提出了融合地学知识的 RBF影像分类模型 ;最后以实际的遥感土地覆盖分类为例 ,通过与 BP神经网络方法 (BPNN )相比较 ,对分类过程和结果进行了综合分析 ,认为 RBF方法在学习速度、网络结构、融合领域知识等方面具有一定的优势

 
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