这种学习可以使用神经网络或者支持向量机，不过用决策树也可以实现类似的功能。
This sort of learning could take place with neural networks or support vector machines, but another approach is to use decision trees.
介绍如何用神经网络进行动作设计、用遗传算法进行避障、用决策树进行决策的快速产生。
The paper gives the methods of the action design using ANN, avoiding the obstacles using EA and making decision using decision-tree.
也许用决策树来学习如何在丛林中勘查是非常愚蠢的，但用它们在餐馆中选取食物却非常合适。
It might be very silly to use decision trees for learning how to actually explore a jungle but very reasonable to use them for picking food at a restaurant.
这种类型的学习通常交给神经网络来完成，虽然很难想象，但用决策树来完成这类问题也很简单。
This type of learning could probably be carried out with neural networks, though it is hard to imagine that the problem is simple enough for decision trees.
用决策树知识作为有关案例知识的索引对案例进行触发，对二者得到的诊断结论进行综合后输出。
Cases are triggered by the index which is gotten from knowledge represented by decision trees and diagnostic conclusions derived from the two approaches are output by synthesize.
经实验证明，用该方法构造的决策树与传统的基于信息熵方法构造的决策树相比较，复杂性低，且能有效提高分类效果。
The experiments show that, compared with the entropy-based method, our method is simpler in the structure, and can improve the efficiency of classification.
用该方法生成的决策树规模小且计算复杂度低，但是泛化能力不佳。
The method can learn smaller trees with lower computational complexity, but its generalization ability is not better.
该算法是用高斯相似度度量协方差矩阵间的距离，并由此测度建立了反映协方差矩阵结构关系的二叉决策树。
Gaussian similarity is used for measuring the distance of different covariance. A binary decision tree is constructed with this measure.
方法：以决策树分类器为工具，用分类正确率衡量辨证一致性。
Methods: the decision tree classifier is used as a tool and the rate of classification accuracy is used to measure the consistency.
决策树是用来解决风险型决策问题时使用的一种分析工具，具体是用树形图来分析和选择行动方案的一种系统分析方法。
Tree of decision is a kind of analyzing tool used to solve the problems of risk's decision. In detail, it is a systematic method using tree derivation to analyze and select action scheme.
最后，给出了实验过程，与用熵作启发式的二叉决策树的比较结果表明了本文算法的有效性。
Finally, the experiment process is introduced, comparing with the binary decision tree based on minimum entropy heuristic information, the results show the algorithm proposed in this paper is valid.
用新的属性选择标准生成的决策树一般具有叶子数目较少，叶子的平均深度也较小，且叶子具有较强的泛化能力。
The decision tree constructed with the new standard of attribute selection has the following characteristics: fewer leaf nodes, fewer levels of average depth, better generalization of leaf nodes.
用新的属性选择标准生成的决策树一般具有叶子数目较少，叶子的平均深度也较小，且叶子具有较强的泛化能力。
The decision tree constructed with the new standard of attribute selection has the following characteristics: fewer leaf nodes, fewer levels of average depth, better generalization of leaf nodes.
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