The necessary conditions for optimal sensor decision rules are derived, and a numerical algorithm is designed for finding the optimal sensor decision rules.
在非理想信道条件下，推导了两部传感器的最优判决规则，并给出了求解最优判决规则的数值迭代算法。
Most data mining tools use rule discovery and decision tree technology to extract data patterns and rules; its core is the inductive algorithm.
大部分数据挖掘工具采用规则发现和决策树分类技术来发现数据模式和规则，其核心是归纳算法。
Dynamic reduction problem is discussed and algorithm adding and reducing rules in the decision-making table dynamically are put foreword so as to reduce the computing complexity.
一方面分析了动态自主知识获取问题中的决策表动态约简问题，确定了在获得基本最小规则集后动态增加或减少规则的算法。
In this paper, we extract rules of the decision table by an incremental algorithm for the dynamic decision table of the time series data.
本文对数据成时间序列的动态决策表，用增量式算法提取决策表的规则模型。
Furthermore, a new algorithm for rule extraction based on decision matrices was presented. And much more concise decision rules could be got with this method.
同时，借助决策矩阵进行值约简，提出了一种新的规则提取算法，使最终得到的决策规则更加简洁。
The algorithm constructs decision tree using an improved ID3 algorithm, and fills the missing data by decision rules.
该算法应用改进的ID 3算法来构造决策树，利用决策规则对缺失值进行补充。
The comparable and analyzable experiment shows that this algorithm can make a minimize decision tree whose rules are true.
通过应用实例比较分析，证明该算法能生成最小化决策树，并且决策树生成规则切合实际。
The new algorithm carries out the reduction processing to the generated decision rules containing the frequency attribute and obtains the simplest decision rules.
新的算法对生成的带频度属性的决策规则进行约简处理，得出最简决策规则。
Finally, a heuristic algorithm for rules extraction of decision tree was designed.
以新的属性重要性为启发式信息设计决策树规则提取方法。
MDRBR algorithm USES uniform minimum support threshold for mining default decision rules, the method can not effectively mine the interesting default decision rules to users.
MDRBR算法采用单一的规则支持度阈值进行缺省规则的挖掘，这不利于有效地挖掘出用户感兴趣的缺省规则。
The optimized ID3 algorithm can process the XML document intelligently, generate the decision rules, and optimize the rules in the limit range.
该改进的算法可以智能处理XML文档，生成决策规则，并在一定范围内优化规则。
At last, a binary decision tree could be built. Algorithm analysis and simulation results show that RMBRDM can support rules with ranges and the performance of RMBRDM is better than that of PTS.
最后建立一棵二叉决策树。理论分析和仿真实验均表明，RMBRDM算法不仅支持以范围形式表示的规则，且时空性能优于PTS算法。
Finally, valuable decision-making information is discovered by using the parallel algorithm based on FP-growth while mining the association rules of online transactions.
利用该算法对网上交易进行关联规则挖掘，发现了有价值的决策支持信息。
This paper gives the rule-support degree a new definition, extends present models and brings forward an algorithm of mining weighted-decision-rules, the result of the experiment shows its effectivity.
通过对规则支持度提出新的定义，对现有的模型进行了扩展，并由此提出了一种新的决策规则挖掘算法，实验结果表明了其有效性。
The necessary conditions for optimum detection for the distributed detection systems are derived, and a numerical iterative algorithm for obtaining the optimum sensor decision rules is presented.
文中给出了融合系统检测性能最优化的必要条件，以及求解最优系统判决规则的数值迭代算法。
The necessary conditions for optimum detection for the distributed detection systems are derived, and a numerical iterative algorithm for obtaining the optimum sensor decision rules is presented.
文中给出了融合系统检测性能最优化的必要条件，以及求解最优系统判决规则的数值迭代算法。
应用推荐