该词条未找到_海词词典

您要查找的是不是：

SVM Decision Tree (SVMDT) SVM决策树
New method for deciding the sort of the test sample based on SVM decision tree 基于SVM决策树判别测试点类别的新方法
Method of designing hierarchical structure of SVM decision tree based on separating measure with degree of membership 基于隶属度分离测度SVM决策树层次结构设计方法
SVM decision tree SVM决策树
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。
Decision tree is a useful method of classification. 摘要决策树是分类的常用方法。
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。
On the basis of analyzing Multi-classification SVM, aiming at the question of a small quantity of image classification, we present a new Multi-SVM classification method based on MLP and unilateral binary decision tree. 在分析多类支撑矢量机分类的基础上，针对总类型数量不多的图像分类情况，提出了基于多层感知器和单向二叉决策树的多类支撑矢量机分类方法。
Decision trees can be used for prediction. 决策树可用于进行预测。
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”。
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。
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。
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。
The traditional decision tree category methods(such as:ID3,C4.5) are effective on small data sets. 传统的决策树分类方法(如ID3和C4.;5)对于相对小的数据集是很有效的。
How to construct the Decision Trees with high precision and small size is core. 如何构造精度高、规模小的决策树是决策树算法的核心内容。