Decision tree induction is a kind of the induction learning.
决策树归纳是归纳学习的一种。
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。
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。
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。
This paper proposes a new algorithm called LAID(learning algorithm as inverse deduction) considering the difficulty of traditional induction learning.
针对传统归纳学习的困难，提出一种新的逆演绎的学习算法。
Most data mining and induction learning methods can only deal with discrete attributes; therefore, discretization of continuous attributes is necessary.
很多数据挖掘方法只能处理离散值的属性，因此，连续属性必须进行离散化。
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。
Mostly used methods are introduced in detail, including fuzzy method, rough sets theory, cloud theory, evidence theory, artificial neural networks, genetic algorithms and induction learning.
详细介绍了数据挖掘技术的常用方法，包括模糊理论、粗糙集理论、云理论、证据理论、人工神经网络、遗传算法以及归纳学习。
This paper presents a model for identifying induction motor speed using the recurrent neural network, which is trained by a real time recurrent learning algorithm.
本文利用递归神经网络来建立异步电机转速辩识模型，其网络学习采用实时递归学习算法。
A statistical inductive learning approach is proposed to investigate GIS attribute data mining. This approach integrates statistical analysis with attribute oriented induction method.
将统计分析方法和面向属性的归纳方法结合起来，形成了一种应用面比较广的统计归纳学习方法，可以用于GIS属性数据挖掘。
Data mining is an intercrossed subject, involving many fields such as machine learning, model reorganization, induction and deduction, statistics, database and high performance calculation.
数据挖掘是一门交叉性学科，涉及机器学习、模式识别、归纳推理、统计学、数据库、高性能计算等多个领域。
The simulation of induction is very important in machine learning. induction is a common object of many science's investigations.
在机器学习中对归纳的模拟是特别重要的。归纳是多种学科的共同研究对象。
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。
Only under solution method induction condition, significant difference can be found between learning one and two source problems.
只有在归纳思路的情况下，学习两个源问题的迁移效果才会显著好于学习单个源问题的迁移效果。
Then, a DTC induction motor servo control simulation model is established, which adopts the self-learning fuzzy in velocity observer.
建立了伺服感应电动机DTC控制模型，其中速度观测器采用文中提出的自学习模糊速度观测器。
Then, a DTC induction motor servo control simulation model is established, which adopts the self-learning fuzzy in velocity observer.
建立了伺服感应电动机DTC控制模型，其中速度观测器采用文中提出的自学习模糊速度观测器。
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