decision tree classifier meaning in Chinese
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
Examples
- 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。 - 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。 - 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。 - 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。 - 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。