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- decision tree classifi cation 决策树分类
- decision tree classify 决策树分类
- Decision tree is a useful method of classification. 摘要决策树是分类的常用方法。
- Wedo not use a pre-defined decision tree to classify the environment in the ND method.Also,we do not solve the reward function in the imitation learning method. 我们并不使用事先定义好的决策树来分类环境，也不试图将奖励函数解出，我们将试著找到环境资讯与人类控制行为的对应关系。
- A decision tree is a graphic model of a decision process. 决策树是描述决策过程的一种图形。
- Swarm Intelligence - Based Selective Ensemble with Decision Trees Classifiers 基于群体智能的选择性决策树分类器集成
- Then we employ Decision Trees and clustering technique to classify the shot into anchor shot or unanchored shot. 然后综合采用决策树和聚类分析方法对镜头分类,判断镜头中是否有主持人,从而实现主持人镜头的检测。
- decision tree classifier 树形判定分类符
- In the Grid pane, click Source and then select TM Decision Tree mining model. 在“网格”窗格中,单击“源”,然后选择“TM Decision Tree挖掘模型”。
- Decision trees can be used for prediction. 决策树可用于进行预测。
- Click Select Model, expand Targeted Mailing, and then choose TM Decision Tree. 单击“选择模型”,展开“目标邮件”,再选择TM Decision Tree。
- This viewer contains two tabs, Decision Tree and Dependency Network. 此查看器包含两个选项卡,即“决策树”和“相关性网络”。
- Evolutionary decision tree method has the advantage of global search. 演化决策树方法将传统的决策树算法与演化算法相结合,具有全局搜索的优点。
- Decision tree, neural networks and Bayesian networks are the main tools of KDD. 决策树、神经网络、Bayesian网络等是当前知识发现的重要工具。
- For example, in a decision tree mining model the viewer will use Cyan to display continuous attributes. 例如,在树挖掘模型中,查看器将使用青色来显示连续属性。
- On the Decision Tree tab, you can examine all the tree models that make up a mining model. 在“决策树”选项卡上,可以检查构成挖掘模型的所有树模型。
- When you build a decision tree model, Analysis Services builds a separate tree for each predictable attribute. 生成决策树模型时,Analysis Services将为每个可预测属性生成一个单独的树。
- One of the best ways to analyze a decision is to use so-called decision trees. 所谓决策树是进行决策分析的最佳方法之一。
- A tree classifier used for QRS complexes pattern classification has the characteristics of rapid classification and marking off a great number of different patterns. 用于QRS波模式分类的树分类器具有分类速度快、区分模式多等特点。
- Now there are many methods that has been applied to this field, such as SVM, KNN, Naive Bayes, Decision Tree, etc. 目前已经有许多方法应用到该领域。 如支持向量机方法(SVM)、K近邻方法(KNN)、朴素贝叶斯方法(Naive Bayes)、决策树方法(Decision Tree)等等。
