tree classifier中文是什么意思

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中文翻译
造句

树状分类器

tree n. 1.树〔主要指乔木，也可指较大的灌木〕。 ★玫瑰可 ...
classifier n. 1.分类者。 2.【矿物】分级机。 3.【化学】 ...
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
decision-tree classifier 树形判定分类法
classifier n. 1.分类者。 2.【矿物】分级机。 3.【化学】分粒器。 4.(汉语等中的)量词。
a tree 树状; 一棵树; 一蔸树
in a tree 在树上
in the tree 在树上（非树本身）
in tree 内树型; 入树
into tree 放在树中
on the tree 长在树上的，如果实一类的。; 在树上(本身生出的); 在树上（果实等）
or tree 或树
s tree 无私奉献的树
tree n. 特里〔姓氏〕。 n. 1.树〔主要指乔木，也可指较大的灌木〕。 ★玫瑰可以称为 bush, 也可以称为 tree. 2.木料，木材；木构件；〔古语〕绞首台；〔the tree〕(钉死耶稣的)十字架；鞋楦。 3.树形(物)，世系图，家系 (＝family tree)；【数学】树(形)；【化学】树状晶体。 a banana tree 香蕉树。 an axle-tree 心棒，轴料。 a boot-tree 靴楦[型]。 a saddle-tree 鞍架。 at the top of the tree 在最高地位。 tree of Buddha 菩提树。 tree of heaven 臭椿。 tree of knowledge (of good and evil) 【圣经】知道善恶的树，智慧之树。 tree of life 生命之树，生命力的源泉【植物；植物学】金钟柏。 up a tree 〔口语〕进退两难，不知所措。 vt. 赶(猎兽等)上树躲避；〔口语〕使处于困境；穷追；把鞋型插入(鞋内)。
adaptive classifier 自适应分类器
air classifier 风力分级器; 风力化级器; 风筛; 空气分级机; 空气分级器; 空气过滤器; 气力分级器; 气流分级机
akins classifier 阿金斯分级机; 螺旋分级机
annular classifier 环式分级机
array classifier 阵列分类器
atkins classifier 阿特金斯选粒机
automatic classifier 自动分类机
bayes classifier 贝斯分类符; 贝叶斯分类器
bayesian classifier 贝叶斯分类器
bowl classifier 分级槽; 浮槽分级机; 浮槽分级器; 浮槽式分级机
cascade classifier 梯级式分选机

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例句与用法

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。
Conception hierarchy tree classifiers which is a statistical approach have played an important role in attribute - oriented induction . it can help us discover the characteristics of data , make them more understandable and organized in concept - oriented structure
通过它对数据库中的数据进行分类可以帮助我们发现数据的特征，以更加容易理解的方式总结数据，并且依据面向概念的结构来组织数据。
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。
Data warehouse is a hot research area in 90s its main motif is to provide the decision - maker a powerful tool : gathering the data in pure consistent , relevant pattern , and making use of the data in managing analyzing , data - mining purposec that means that the decision - maker can use the tool to understand , grasp the situation of the business from different directions and forecast the future of it when using data warehouse , the processing speed determines data warehouse ' s practicability and processing ability the hoc ( highway decision center ) system realized before solves some key problems about intermediate scale data , mainly concentrating data warehouse performance coefficient when using hdc in large scale data , it encountered processing speed problem then the settlement of this problem becomes a major research point so , based on the former research achievements , the present task is to construct the renowned data warehouse architecture and its relevant algorithms , then adapts the system to the large scale dataset with data mining functions c this paper is a part of the research in order to construct the powerful system , a key problem is to cope with the processing - speed problem and the data space problem , etc , - caused by the large scale dataset and magnificent dataset this is also the core in the present data mining research this paper ' s motive is to design and realize a decision - tree classifier in the data warehouse system for large - scale dataset
大型数据仓库的处理速度问题目前是制约其推广应用的关键所在，也是这一领域的一个重要研究课题，也正是我们当前工作的重点：在前期研究工作的基础上围绕提高大型数据仓库处理速度问题，建立改进的数据仓库系统模型和相关算法，开发出面向中级以上企事业单位的、具有数据挖掘和分析能力的大型数据仓库系统。建立大型数据仓库所面临的关键问题，是如何妥善解决实际业务数据的大规模、海量特征所带来的处理速度和空间等问题，这也是当前挖掘技术研究必然面对的核心问题。本研究的目的是设计并实现大型数据仓库系统中的分类数据挖掘工具? ?决策树分类器，主要工作是在综合了解现有决策树分类算法的研究情况的前提下，对决策树算法适应大规模数据集的问题进行探讨，力求设计出能较好地适应大规模数据的分类器算法。