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规则提取     
相关语句
  rule extraction
    MATRIX COMPUTATION FOR DATA CLEANING AND RULE EXTRACTION IN INFORMATION SYSTEM
    信息系统数据清洗、规则提取的矩阵算法
短句来源
    An Incremental Rule Extraction Algorithm
    一种增量式规则提取算法
短句来源
    The Construction of Weighted Concept Lattice and Association Rule Extraction
    加权概念格的渐进式构造及其关联规则提取
短句来源
    Hierarchical particle swarm optimization algorithm and its application in classification rule extraction
    层次化粒子群优化算法及其在分类规则提取中的应用
短句来源
    Research on pathology diagnosis rule extraction algorithm based on rough sets theory
    基于粗糙集理论的病理诊断规则提取算法研究
短句来源
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  rules extraction
    Fuzzy Rules Extraction Based on Soft Clustering and Kalman Filter Method
    基于软分类和卡尔曼滤波方法的模糊规则提取
短句来源
    Decision Rules Extraction Based on Rough Set
    基于Rough集的决策规则提取
短句来源
    2. Lucubrating in RS(Rough Set) TheoryIn the system, an integrated algorithm used to analysis slice up data (from UCI database) is proposed and implemented, which is based on the attributes reduction and rules extraction, obtaining the core and effective rules, which are more comprehend than original data, and can help the doctor to understand patient's state of the illness quickly.
    提出并实现了一个属性约简和规则提取的集成算法。 将此集成算法应用于生理切片数据(来源于UCI数据库)并提取出精简规则,这些规则相比原数据更利于人们理解,并且有助于医生快速了解病人病情。
短句来源
  rule extracting
    Knowledge discovery especially rule extracting can regard as a process of concept forming and a process of finding the relations between concepts.
    在基于概念格的规则提取方面,知识发现尤其是规则挖掘可以看作是一个形成概念的过程和发现概念之间关系的过程。
短句来源
    A new and more advantageous lattice structure for rule extracting is proposed based on the theory of concept lattice and the concept of closed item set in this thesis. Then, An incremental algorithm based on closed label for constructing lattice and algorithm for rules extracting are developed.
    本文基于概念格理论和闭项集的概念,提出了一种新的更有利于规则提取的格结构,给出了相应的基于闭标记的渐进式构造算法和规则提取算法。
短句来源
  rule acquisition
    Decision Rule Acquisition on Fuzzy Information System
    模糊信息系统上的规则提取
短句来源
    Rule acquisition of inconsistent decision table
    不协调决策表的规则提取
短句来源
    Research on the Attribute Reduction and Rule Acquisition of Time-Series Data Based on Rough Entropy
    基于粗糙熵的时序数据属性约简及规则提取研究
短句来源

 

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  rule extraction
A rule extraction tech nique is then applied in order to extract explicit knowledge from the trained neural networks and represent it in the form of fuzzy if-then rules.
      
Our proposed network adapts from an initial analog phase, which has good training behavior, to a discrete phase for automatic rule extraction.
      
First, Radial Basis Function Neural Networks (RBFNN) learning techniques are explored, as is usual in the literature, since the local nature of this paradigm makes it a suitable platform for performing rule extraction.
      
By using support vectors from a learned SVM it is possible in our approach to use any standard Radial Basis Function (RBF) learning technique for the rule extraction, whilst avoiding the overlapping between classes problem.
      
Linguistic Rule Extraction From a Simplified RBF Neural Network
      
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  rules extraction
Rules extraction in short memory time series using genetic algorithms
      
A generic fuzzy aggregation operator: rules extraction from and insertion into artificial neural networks
      
A Dual-Objective Evolutionary Algorithm for Rules Extraction in Data Mining
      
Association rules extraction between attributes based on the above mentioned classification scheme.
      
Based on rough set theory many algorithms for rules extraction from data have been proposed.
      
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  rule acquisition
With the new definition of approximation to an object set and the concept of attribute value pair, rough-sets-based methodology for certain rule acquisition in an incomplete information system is developed.
      
Rough sets theory has been employed for diagnostic rule acquisition of rotating machinery with consideration of conflicting objects of decision table.
      
Rule acquisition is guided by the constraints of physiological process models represented in the system.
      
An optional activity in this phase is Business rule acquisition.
      
An important source for the rule acquisition was the old system.
      
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