Chinese translation for "decision tree classifier"

决策树分类器
树型判定分类程序
树形判定分类程序
树形判定分类法

Related Translations:

structured classifier:  结构化类元

important decision:  重大决策

decision machine:  决策机决策计算机

decision verifiability:  决策可证实性

great decision:  重大决策

carterfone decision:  卡特尔电话决定

decision region:  决策区域判定域

decision tree:  决策树图决策体系决策图表, 分层次决策决策宗谱判定树判断树

funding decision:  分配资金决策

binary decision:  二元判定, 双值决策双择判决

Example Sentences:

	1.	On the basis of analyzing the classification principle of decision tree classifier and parallelpiped classifier , a new classification method based on normalized euclidian distance , called wmdc ( weighted minimum distance classifier ) , was proposed 通过分析多重限制分类器和决策树分类器的分类原则，提出了基于标准化欧式距离的加权最小距离分类器。
	2.	A decision tree classifier using a scalable id3 algorithm is developed by microsoft visual c + + 6 . 0 . some actual training set has been put to test the classifier and the experiment shows that the classifier can successfully build decision trees and has good scalability 最后着重介绍了作者独立完成的一个决策树分类器。它使用的核心算法为可伸缩的id3算法，分类器使用microsoftvisualc + + 6 . 0开发。
	3.	It is demonstrated by simulation data . as for classifier , it presents the artificial neural network . based on three methods of modulation recognition and decision tree classifier and neural network classifier , experimentations have been carried through 在分类器设计方面,介绍了利用神经网络进行模式识别的原理,采用前述的三种特征提取方法,分别结合判决树分类器和神经网络分类器对信号进行分类,并且进行了试验论证。
	4.	Decision tree models are simple and easy to understand , easily converted into rules . it also can be constructed relatively fast compare to some of other methods . moreover , decision tree classifiers obtain similar and sometimes better accuracy when compared with some of other classification methods 与其他分类算法相比，它能够较快的建立简单、易于理解的模型，容易转换成规则，而且具有与其他分类模型同样的，有时甚至更好的分类准确性。
	5.	This paper first illustrated some typical algorithms for large dataset , then gave off a processing diagram in common use second , for the dataset with large quantity and many attributes , we renovated the calculation method of the attribute ' s statistic information , giving off a ameliorated algorithm this thesis consists of five sections chapter one depicts the background knowledge and illustrates the position of data mining among many concepts also here is the data mining ' s category chapter two describes the thought of classification data mining technique , puts forward the construction and pruning algorithms of decision tree classifier chapter three discusses the problems of adapting data mining technique with large scale dataset , and demonstrates some feasible process stepso also here we touches upon the combination r - dbms data warehouse chapter four is the design of the program and some result chapter five gives the annotation the conclusion , and the arrangement of future research 本论文的组织结构为：第一章为引言，作背景知识介绍，摘要阐述了数据挖掘在企业知识管理、泱策支持中的定位，以及数据挖掘的结构、分类；第二章讲述了分类数据挖掘的思路，重点讲解了泱策树分类器的构建、修剪，第三章针对大规模数据对数据挖掘技术的影响做了讲解，提出了可采取的相应的处理手段，以及与关系数据库、数据仓库结合的问题；第四章给出了论文程序的框架、流程设计，以及几个关键问题的设计；第五章对提出的设计进行简要的评述，做论文总结，并对进一步的研究进行了规划。

Similar Words：

"decision to cancel" Chinese translation, "decision to participate" Chinese translation, "decision training" Chinese translation, "decision tree" Chinese translation, "decision tree analysis" Chinese translation, "decision tree diagram" Chinese translation, "decision tree learning" Chinese translation, "decision tree method" Chinese translation, "decision tree structure" Chinese translation, "decision tree system" Chinese translation