图2——代理服务器遍历决策树。
您应该使用规则集或决策树来定义一组规则吗？
Should you use a rule set or a decision tree to define a group of rules?
监督学习是训练神经网络和决策树的最常见技术。
Supervised learning is the most common technique for training neural networks and decision trees.
决策树学习是应用最广泛的归纳推理算法之一。
Decision tree learning is one of the widely used and practical methods for inductive inference.
决策树以图形方式描述导致某项操作的相关条件链。
Decision trees graphically depict chains of dependent conditions leading to an action.
ID 3算法是最基本的决策树学习算法，有很广的应用。
ID3 algorithm is the most basic algorithm in the decision tree learning, and has a wide application.
提出了一种避免了多值偏向问题的决策树算法——AF算法；
Second, this paper proposes a new decision tree algorithm, AF algorithm, which avoids multivalue bios.
试验结果表明，基于度量的决策树的性能优于传统的决策树。
Results indicate that the performance of the metric-based decision tree is some better than that of the conventional decision tree.
决策树提供了序列if - then规则集的另一种表示形式。
Decision trees provide an alternative representation of sequential if-then rule sets.
对这个决策树使用此人的这些属性就可以确定他购买M5的可能性。
The attributes of this person can be used against the decision tree to determine the likelihood of him purchasing the M5.
在构造决策树的过程中，分离属性选择的标准直接影响分类的效果。
In the process of constructing a decision tree, the criteria of selecting partitional attributes will influence the efficiency of classification.
这个决策树中的第三步就是询问数据包是否可扩展，这和定制性刚好相反。
The third step in the decision tree asks if the package is extensible as opposed to customizable.
多值偏向可能导致从数据集中归纳出错误的知识，使决策树的性能下降。
Multivalue BIOS may result in inducing wrong knowledge from data set, and consequently result in the decline of the performance of decision tree.
通过对训练数据的学习，生成用于轨道故障判决的决策树(或者规则)。
The decision tree (or rules) used for rail deformation detection was generated by learning the train data.
决策树算法通过构造精度高、小规模的决策树采掘训练集中的分类知识。
Decision tree algorithm is that the category knowledge of the training set is mined through built high precision and small-scale decision tree.
实验结果表明，应用GP决策树算法能够正确完成对趋势预测模型的选择。
Experimental results show that the choice for trend forecasting models can be correctly finished by using GP-decision tree algorithm.
规则可以在“ifthen”结构、决策表或决策树中描述决定性的决策。
Deterministic decisions where rules can be described in "if then" constructs, decision tables or decision trees.
本文提出一个有效的算法，先构造决策树，然后将构造的决策树转换为神经网。
This paper proposes an efficient algorithm for constructing decision tree and then mapping it to neural net.
通过对决策树分类算法的比较，本文采用C4.5决策树算法实现自学习模块。
Comparing with Decision Tree algorithms, this system chooses the C4.5 to realize the self-learning module.
利用传统的决策树方法，无法确定一个次优方案，对所有方案进行排序也很难。
It is difficult to use the decision tree method to find the sub-optimal solution, not to mention the alternative ranking.
介绍了决策树法的原理、决策步骤，举例说明了决策树法在施工投标中的应用。
The principle and the decision steps of decision tree analysis method are introduced; at the same time the application of this method in construction bidding is illustrated.
然后我们如下建立我们的决策树：，每一个节点，好的，让我们在这里举一个例子。
And then we'll construct our tree as follows: each node, well, let me put an example here.
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.
至今已经提出了决策树的很多算法，通过分析已知的分类信息得到一个预测模型。
So far, there are many algorithms have been given and we can gain a prediction model by analyzed known catalog information.
因此样式表编译器构造了决策树，在运行时用它来决定将哪个模板规则应用于给定节点。
The style sheet compiler therefore constructs a decision tree which is used at run time to decide which template rule to apply to a given node.
我是否该服用预防药？在汤玛斯·戈茨的新书《决策树》中，另有一套简单规则可依循。
Do I need to take preventive drugs?, there's another set of simple rules in the new book the Decision Tree, by Thomas Goetz.
几乎所有业界领先的BRM工具都提供的最常用构件是规则和规则集模板、决策表和决策树。
The most common artifacts provided by almost every industry-leading BRM tool are rule and rule-set templates, decision tables, and decision trees.
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.
当涉及到复杂的、嵌入式软件时，就会导致一个上千——或许上百万种可能分配的多对多决策树。
When complex, embedded software is involved, this leads to a many-to-many decision tree with thousands — perhaps millions — of possible allocations.
当涉及到复杂的、嵌入式软件时，就会导致一个上千——或许上百万种可能分配的多对多决策树。
When complex, embedded software is involved, this leads to a many-to-many decision tree with thousands — perhaps millions — of possible allocations.
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