This sort of learning could take place with neural networks or support vector machines, but another approach is to use decision trees.
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。
Supervised learning is the most common technique for training neural networks and decision trees.
监督学习是训练神经网络和决策树的最常见技术。
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.
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。
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.
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。
We'll discuss validation sets when we look at decision trees because they are a common optimization for decision tree learning.
当我们在后面具体提及决策树时，将会进一步讨论验证集，因为它通常是决策树学习的最优选择。
By the method of learning from examples, DKAS inductively acquires knowledge, in representation of decision trees, from large amount of experience data.
DKAS系统采用示例式学习方法，从大量经验数据归纳获取知识，知识表示为决策树形式。
Simplifying trees is the key part of decision tree induction learning.
树的简化是决策树归纳学习中关键的部分。
In the process of constructing decision trees, DKAS takes advantage of background knowledge about structured attributes to direct learning, so the quality of learning improves.
决策树构造过程中利用关于结构化属性的背景知识指导学习过程，有效地提高了学习质量。
The algorithm of decision trees is well known due to simpleness and easy to realize in machine learning.
在各种机器学习算法中，决策树以其简单容易实现等特点被认可。
Their success sparked a renewed interest in learning AI methods such as decision trees, neural networks, genetic algorithms, and probabilistic methods.
这些游戏的成功从新燃起了对游戏ai方法的热情，比如：决定树，神经元网络，遗传算法，和盖然论。
Decision trees and their ensembles are popular methods for the machine learning tasks of classification and regression.
决策树和决策树的组合，是解决分类问题和回归问题比较流行的一类算法。
Decision trees and their ensembles are popular methods for the machine learning tasks of classification and regression.
决策树和决策树的组合，是解决分类问题和回归问题比较流行的一类算法。
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