This paper introduces the classification model of random decision tree and how to heuristic selected the depth and the number, the experiment shows that the algorithm is effectiveness and efficiency.
该文介绍了随机决策树分类模型及如何启发式选择随机决策树的深度及棵树，通过实验证明了该算法的有效性和高效性。
Decision tree is an important method in induction learning as well as in data mining, which can be used to form classification and predictive model.
决策树是归纳学习和数据挖掘的重要方法，通常用来形成分类器和预测模型。
Using teacher images and machine learning method, an image direction classification model is built as a decision tree. Test results argued the validity of this method.
实验结果表明，系统所使用的轮廓线向量图像特征也能够较有效地应用于图像方向分类，而机器学习则能够有效地为之建立决策树分类模型。
Knowledge discovery component take SLIQ as basic arithmetic. It provides classification model in two stage forms which aim at training suit. That is decision tree and productive rule.
知识发现，采用SLIQ快速可伸缩算法，分类模型用判定树与产生式规则两种形式描述。
Two algorithms for structuring decision tree model of multi-dimensional time series classification were presented.
并给出了两种构造多维时间序列分类的决策树模型算法。
Two algorithms for structuring decision tree model of multi-dimensional time series classification were presented.
并给出了两种构造多维时间序列分类的决策树模型算法。
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