When analyze financial factor, decision tree method on the basis of inductive reasoning means is adopted to analyze the infection of financial factor to loan risk classification.
在财务因素分析上，采用基于归纳推理方法的决策树方法，分析了财务因素对贷款风险分类的影响。
This paper proposes a text classification method based on Cloud Theory and neural network structure decision tree.
提出一种基于云理论和神经网络构造决策树的文本分类方法。
The results of experiment demonstrate that the SVM decision tree built up by this method has a good classification performance.
实验结果表明，这种方法构造的SVM决策树分类器分类性能较好。
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。
In this paper, a new method which combines unsupervised and supervised learning strategy is put forward to construct the multi classification decision tree.
提出了一种融合无监督和监督两种学习策略生成多分类决策树的方法。
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。
This D-S decision tree is a new classification method adapted to the uncertain data.
实验结果表明D- S决策树分类算法能有效的对不确定数据进行分类。
Absrtact: The decision tree is a usual method of classification in data mining.
摘要：决策树是数据挖掘任务中分类的常用方法。
The decision tree is a frequently-used classification method in data mining, it is easy to understand and large in scope to apply.
决策树是数据挖掘技术中一种常用的分类方法，易于理解，应用范围广泛。
The autuors present a decision tree based method for an easier and more accurate land-use classification using apparent reflectance values derived from ASTER images.
以黑龙江省北安市为研究区域，尝试利用ASTER视反射率值进行便利、准确的土地利用分类研究。
This D-S decision tree is a new classification method applied to uncertain data and shows good performance and can efficiently avoid combinatorial explosion.
实验结果表明，D - S证据理论决策树分类算法能有效地对不确定数据进行分类，有较好的分类准确度，并能有效避免组合爆炸。
This D-S decision tree is a new classification method applied to uncertain data and shows good performance and can efficiently avoid combinatorial explosion.
实验结果表明，D - S证据理论决策树分类算法能有效地对不确定数据进行分类，有较好的分类准确度，并能有效避免组合爆炸。
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