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1)  CART [英][kɑ:t]  [美][kɑrt]
CART决策树
1.
Five classification methods which are MLC(Maximum Likelihood Classifier),Parzen window,CART decision tree,BP neural network and Fuzzy ARTMAP neural network are selected to map the land cover of Huabei Area in China using MODIS 250m data.
应用MODIS 250m分辨率遥感影像对中国华北地区分别采用最大似然法、Parzen窗、CART决策树、BP神经网络F、uzzy ARTMAP神经网络等5种分类方法进行区域尺度上土地覆盖制图的比较试验。
2)  decision trees
决策树
1.
Constructional algorithm of principal and subordinate structure self-set based on decision trees;
基于决策树的主从结构的Self集构造算法
2.
Research on the performance of Decision Trees on IR;
决策树在信息检索中的性能研究
3.
Classification rules for mining tumors and normal tissues using genetic algorithms and decision trees;
基于遗传算法和决策树的肿瘤分类规则挖掘
3)  Decision-making tree
决策树
1.
Yield mapping analysis methods in precision agriculture based on decision-making tree modeling and map-overlapping;
基于决策树和图层叠置的精准农业产量图分析方法
2.
This paper first proposes a decision-making tree construction method base on information gain,then gives the corresponding algorithm through one example.
决策树数据挖掘技术是目前最有影响和使用最多的一种数据挖掘技术。
4)  decision tree
决策树
1.
Analysis on the loan repayment behavior of housing loan based on decision tree;
基于决策树的住房贷款还贷行为分析
2.
Application of decision tree in basic medical insurance;
决策树算法在基本医疗保险中的应用研究
3.
An rules extraction algorithm of decision tree based on rough set theory;
基于粗糙集的决策树规则提取算法
5)  decision tree
决策树法
1.
For the special order products stock, the decision tree was introduced.
针对有长期稳定需求的产品的库存管理问题提出了"经济生产批量模型";针对特殊定单产品的库存问题,介绍了决策树法。
2.
This article will approach the practical problems of applying Decision Tree to the Annual Income System.
本文拟探讨采用决策树法在年薪制中的应用问题,并对具体操作过程进行了设定和说明。
3.
Then the classification image from the method is compared with that from the decision tree method.
因此,提出了混合像元分解模型结合神经网络法(MPD-NN法),利用其对北京市TM图像进行地物分类,并与较常用的决策树法分类结果比较,研究在图像现有空间分辨率的条件下提高城市分类精度。
6)  C4.5 decision tree
C4.5决策树
1.
Costume sales forecasting model based on CURE algorithm and C4.5 decision tree;
基于CURE算法和C4.5决策树的服装销售预测模型
补充资料:决策树
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性质: 一种可用于处理多阶段决策问题的决策图。由于这种图形似树枝,故称为决策树。它由决策点,方案技,概率点(又称状态点),概率枝(又称状态枝)顺序延伸而成,最右端是益损值见图(图暂缺)。决策时,从右至左,先算出各个概率点的益损期望值,并分别标注在各概率点上。然后对各概率点(即方案)的益损期望加以比较,即选出最大的益损期望值并标注在决策点的上方、与最大期望值相应的即为最优方案,然后决定解的去留,直到最后找到选好解。

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