Decision tree learning is one of the widely used and practical methods for inductive inference.
决策树学习是应用最广泛的归纳推理算法之一。
ID3 algorithm is the most basic algorithm in the decision tree learning, and has a wide application.
ID 3算法是最基本的决策树学习算法，有很广的应用。
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。
And it introduces some algorithms of decision tree learning such as ID3, C4.5 and feature subset selection of Inductive learning.
介绍了归纳学习中的决策树学习算法如id3、C4.5和特征子集选择问题。
Decision tree learning strategy have long been popular in pattern recognition, machine learning, and other disciplines for solving problems concerned with the classification.
决策树学习策略广泛应用于模式识别和机器学习等领域，用来解决与分类相关的问题。
The decision tree (or rules) used for rail deformation detection was generated by learning the train data.
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)。
Induction learning of decision tree based on ID3 algorithm is an important branch of inductive learning now, which can be used to automatic acquisition of knowledge.
基于ID 3算法的决策树归纳学习是归纳学习的一个重要分支，可用于知识的自动获取过程。
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。
Comparing with Decision Tree algorithms, this system chooses the C4.5 to realize the self-learning module.
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。
Minimum entropy is chosen as a heuristic strategy in decision tree (DT) learning algorithm such as ID3.
决策树的学习算法，比如id3算法，选用最小信息熵作为启发式信息。
The decision tree method is the effective method of detecting for concept describing space and the representative learning way in exampling learning of which specially dispose mass object.
决策树方法是发现概念描述空间的一种特别有效的方法，是实例学习中具有代表性的学习方法，专门用于处理大量对象。
Based on decision tree combined strategy and multiple kernel learning support vector machines, a new fault diagnosis method is proposed to improve the precision and speed of fighter fault diagnosis.
为了提高歼击机故障诊断的准确性与实时性，提出一种基于决策树型组合策略的多重核学习支持向量机诊断方法。
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。
Decision tree is a basic learning method in machine learning and data mining.
决策树是机器学习和数据挖掘领域中一种基本的学习方法。
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。
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。
Decision tree simplification is a significant branch in the study of decision-tree learning algorithms.
决策树简化是决策树学习算法中的一个重要分支。
A new parallel learning method of the decision tree is proposed in this paper.
提出一种并行的决策树学习方法。
Rough Set and Decision Tree, usually used to analyze the data and the formation of predictive models, are important methods of knowledge discovery and learning.
粗糙集和决策树是知识挖掘和学习的重要方法，通常用来分析数据和形成预测模型。
The rule based expert system, which regarded the decision tree as the learning mechanism has the characteristic of comprehensible flexible and constructed rapidly.
以决策树作为学习机制构建以规则为中心的专家系统，具有易理解性、灵活性，构造速度快等特点。
Decision tree induction is a kind of the induction learning.
决策树归纳是归纳学习的一种。
The new algorithm combines the merit of decision tree induction method and naive Bayesian method. It retains the good interpretability of decision tree and has good incremental learning ability.
该算法综合了决策树方法和贝叶斯方法的优点，既有良好的可解释性，又有良好的增量学习能力。
In this paper, we study the active learning method based on the incremental decision tree through which combines the merits from the incremental learning and the active learning.
本文研究了基于增量决策树的主动学习方法，其实就是将增量学习和主动学习两种方法进行有效地结合，从而同时发挥二者的优势。
Decision tree is an important method for data mining as well as induction learning.
决策树是数据挖掘和归纳学习的重要方法。
This paper presents two learning algorithms which can implement the ability of increasing knowledge in decision tree and the ability of learning from misjudgement.
本文详细地给出了决策树的两种学习算法，实现了决策树中知识的增长以及从误判中学习新的知识的功能。
In this paper, a new approach is set forth that integrating both decision tree incremental learning and neural network global learning. Through theory analysis, it's indicated th…
为解决该问题，本文采用了决策树增量学习法和神经网络完全学习相结合的方法。
In this paper, a new approach is set forth that integrating both decision tree incremental learning and neural network global learning. Through theory analysis, it's indicated th…
为解决该问题，本文采用了决策树增量学习法和神经网络完全学习相结合的方法。
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