模糊决策树的英文

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"模糊决策树"怎么读用"模糊决策树"造句

英文翻译手机版

fuzzy decision-tree

"模糊"英文翻译 dim; vague; indistinct; obsc ...
"决策"英文翻译 yes-no decision
"树"英文翻译 tree
"模糊决策" 英文翻译 : fuzzifying decision; fuzzy decision
"决策树" 英文翻译 : decision tree learning; decisiontree; fdt; learning decision trees
"topliss决策树法" 英文翻译 : topliss decision approach
"简化决策树" 英文翻译 : reduced decision tree
"决策树法" 英文翻译 : decision tree method
"决策树方法" 英文翻译 : decision trees
"决策树分析" 英文翻译 : decision tree analysis
"决策树图" 英文翻译 : decision tree diagram
"决策树图解" 英文翻译 : decision-tree diagram
"决策树系统" 英文翻译 : decision tree system
"决策树组" 英文翻译 : decision trees
"条件决策树" 英文翻译 : conditional decision tree; tree
"子决策树" 英文翻译 : sub-tree
"决策树分类器" 英文翻译 : decision tree classifier
"决策树分析图" 英文翻译 : deision tree diagram
"决策树归纳法" 英文翻译 : dtl-decision tree induction
"决策树中结果" 英文翻译 : consequences in decision tree
"决策树状结构" 英文翻译 : decision tree structure
"决策树中的结果" 英文翻译 : consequence in decision tree
"随机决策树分析" 英文翻译 : stochastic decision tree analysis
"模糊合成决策" 英文翻译 : fuzzy synthetic decision
"模糊综合评判；模糊合成决策" 英文翻译 : fuzzy synthetic decision

例句与用法

Because building optimal fuzzy decision tree is np - hard , it is necessary to study the heuristics
由于构建最优的模糊决策树是np - hard ，因此，针对启发式算法的研究是非常必要的。
There are some questions that need be deeply researched . for example , the material application of dynamic fuzzy decision tree , the operation of decision rules etc
尽管如此,本文的工作还很基础,今后还有许多工作需做进一步研究,如动态模糊决策树的具体应用、规则的提取等。
But , fuzzy decision tree induction is an important way for learning from examples with fuzzy representation . it is a special case of fuzzy decision tree induction extracting rules from the data , which have symbol features and crisp classes
而模糊决策树归纳是从具有模糊表示的示例中学习规则的一种重要方法，从符号值属性类分明的数据中提取规则可视为模糊决策树归纳的一种特殊情况。
( 2 ) the reasonable describing about dynamic fuzzy decision tree from attributes treatment to building the tree and then pruning tree , and it provides a certain extent stated theory foundation for ulteriorly researching dynamic fuzzy decision tree and establishes a main concept frame of dynamic fuzzy decision tree
( 2 )对动态模糊决策树从属性处理到构建以及剪枝给出了合理的描述,形成了动态模糊决策树的基本概念框架。
By analyzing expression between a and fuzzy entropy from the view of analytics , this paper analyses the relationship of between a and fuzzy entropy and the changing trend of fuzzy entropy function with the increase of a , then discusses the sensitivity of the parameter a to classification result such as total nodes , rule number , classification accuracy of fuzzy decision tree , proposes an experimental method of obtaining optimal a , it is proved by experiment that the optimal value a obtained by this method can make the classification result of fuzzy decision tree best , and therefore provides the academic evidence of selecting parameter a in order to gain the best classification result
本文在visualc + +软件开发平台及模糊id3算法的基础上，从解析的角度出发，通过分析参数与模糊熵之间的函数关系式，讨论了随着的增加，模糊熵函数的变化趋势，进一步分析了参数对模糊决策树的分类结果在训练准确率、测试准确率、规则数等方面所表现出的敏感性，探讨了得到最优参数的实验方法。实验证明，利用这一方法得到的最优参数的值，可以使模糊决策树的分类结果达到最好的效果，从而为人们用模糊决策树进行分类时选取参数以获得最优的分类结果，提供了良好的理论依据。
In building fuzzy decision tree , each expanded attribute ca n ' t classify the class label clearly like decision tree , but the cases covered with the attribute - values have some overlap . so the entire process of building trees is based on a significant level a , the import of a can reduce such overlap in some degree , decrease the uncertainty of classification and improve classification result
在模糊决策树的产生过程中，用模糊熵选择的扩展属性不能像经典决策树那样将类清晰的分开，而是属性术语所覆盖的例子之间有一定的重叠，因此树的整个产生过程在给定的显著性水平的基础上进行，参数的引入能在一定程度上减少这种重叠，从而减少分类的不确定性，提高模糊决策树的分类结果。
Network forensics is an important extension to present security infrastructure , and is becoming the research focus of forensic investigators and network security researchers . however many challenges still exist in conducting network forensics : the sheer amount of data generated by the network ; the comprehensibility of evidences extracted from collected data ; the efficiency of evidence analysis methods , etc . against above challenges , by taking the advantage of both the great learning capability and the comprehensibility of the analyzed results of decision tree technology and fuzzy logic , the researcher develops a fuzzy decision tree based network forensics system to aid an investigator in analyzing computer crime in network environments and automatically extract digital evidence . at the end of the paper , the experimental comparison results between our proposed method and other popular methods are presented . experimental results show that the system can classify most kinds of events ( 91 . 16 ? correct classification rate on average ) , provide analyzed and comprehensible information for a forensic expert and automate or semi - automate the process of forensic analysis
网络取证是对现有网络安全体系的必要扩展,已日益成为研究的重点.但目前在进行网络取证时仍存在很多挑战:如网络产生的海量数据;从已收集数据中提取的证据的可理解性;证据分析方法的有效性等.针对上述问题,利用模糊决策树技术强大的学习能力及其分析结果的易理解性,开发了一种基于模糊决策树的网络取证分析系统,以协助网络取证人员在网络环境下对计算机犯罪事件进行取证分析.给出了该方法的实验结果以及与现有方法的对照分析结果.实验结果表明,该系统可以对大多数网络事件进行识别(平均正确分类率为91 . 16 ? ) ,能为网络取证人员提供可理解的信息,协助取证人员进行快速高效的证据分析