The decision trees are similar structure to influence diagrams.
决策树与影像图网的结构类似。
This paper applies attribute-oriented induction and decision trees(C4.
论文利用面向属性归纳和决策树C4。
Choose factors from different aspect, analyze data, get decision trees.
筛选不同方面的因素，经过数据分析，得到决策树。
One of the best ways to analyze a decision is to use so-called decision trees.
决策树是一种采用分治策略的聚类分析方法，构建决策树的关键是选择合适的属性。
Decision trees provide an alternative representation of sequential if-then rule sets.
决策树提供了序列if - then规则集的另一种表示形式。
Decision trees graphically depict chains of dependent conditions leading to an action.
决策树以图形方式描述导致某项操作的相关条件链。
This approach has been extensively investigated for association rules and decision trees.
这种方法在关联规则和决策树中得到了广泛的研究。
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。
Some classical clustering algorithms and decision trees algorithms are analyzed and compared.
并具体分析比较了多种的典型聚类和决策树数据挖掘算法。
A new heuristic function to build decision trees based on variable precision rough set is proposed.
应用变精度粗糙集理论，提出了一种利用新的启发式函数构造决策树的方法。
We put them into IF-THEN and Decision Trees by some methods. We also discuss how to learn in rule sets.
接下来讨论了规则的表示问题：决策树的获取方法和IF -THEN规则表达法，新例对规则的学习等问题。
This involves extracting simple key points and using a collection decision trees to classify the image.
这涉及到提取简单的关键点，并使用集合决策树的图像进行分类。
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。
This is a very important concept, and we'll see a lot of algorithms essentially implement decision trees.
这是一个很重要的概念，我们可以看到很多，基于决策树的算法。
Ensemble of complete random trees, i. e. decision trees without any split selection, has high performance.
以完全随机树(不包含属性选择过程的决策树)作为基学习器的集成，具有很好的性能。
Deterministic decisions where rules can be described in "if then" constructs, decision tables or decision trees.
规则可以在“ifthen”结构、决策表或决策树中描述决定性的决策。
Generally, the learned model can represent by classification rules, decision trees, or mathematical formulae.
通常，模型可以用分类规则、判定树或数学公式表示。
Verb decision trees are powerful tools to generalize and extract knowledge from database containing time series.
对于从包含时间序列的数据库中归纳和提取知识而言，计算动词决策树是一个强大的工具。
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。
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。
For modeling of medical ward based on data fusion and data mining, multi - layer perceptron network and decision trees are used.
在结合数据融合和数据挖掘的医疗监护模型的建模方面，采用多层感知器网络和决策树方法建立报警决策器的模型。
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。
Using decision trees in expert systems is nothing new, but applying that idea to a crowdsourcing model might possibly be a stroke of genius.
在专家系统里使用决策树并非什么新鲜事物，但是把这种想法和大众外包（crowdsourcing）模式结合在一起实属天才之作。
The most common artifacts provided by almost every industry-leading BRM tool are rule and rule-set templates, decision tables, and decision trees.
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。
This paper pointed out the shortages of reasoning algorithms of the present belief decision trees and then proposed a new reasoning algorithm.
指出现有信度决策树中推理算法的不足之处，给出了一种新的基于规则的推理算法。
Now I have done some basic reading on supervised and unsupervised learning algorithms such as decision trees, clustering, neural networks... etc.
现在我已经做了对的监督和无监督学习算法，如决策树，一些基本的阅读聚类，神经网络等。
Though there are some shortages in rules setting and parameter optimization, the computational verb decision trees are powerful and useful tools.
虽然其在规则设置与参数优化上还是存在着一些不足，但仍可以看出计算动词决策树是一个非常强大和有效的工具。
The Repository Method starts where genetic programming ends: decision trees produced by EDDIE can be further processed to improve their performance.
遗传规则的结束标志着智能算法的开始：从EDDIE得到的决策图标能进一步证明遗传规则的作用。
This thesis expounds current primary database, data warehouse optimization theory, decision trees and decision support systems critical technologies.
本论文中论述了当前主要的数据库、数据仓库优化理论和决策支持系统的关键技术。
This thesis expounds current primary database, data warehouse optimization theory, decision trees and decision support systems critical technologies.
本论文中论述了当前主要的数据库、数据仓库优化理论和决策支持系统的关键技术。
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