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tree classifier in Chinese

Pronunciation:
How to pronounce "tree classifier""tree classifier" in a sentence

Translationmobile phoneMobile

  • 樹狀分類器

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
    通過分析多重限制分類器和決策樹分類器的分類原則，提出了基于標準化歐式距離的加權(quán)最小距離分類器。
  • 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
    最后著重介紹了作者獨立完成的一個決策樹分類器。它使用的核心算法為可伸縮的id3算法，分類器使用microsoftvisualc + + 6 . 0開發(fā)。
  • 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
    通過它對數(shù)據(jù)庫中的數(shù)據(jù)進行分類可以幫助我們發(fā)現(xiàn)數(shù)據(jù)的特征，以更加容易理解的方式總結(jié)數(shù)據(jù)，并且依據(jù)面向概念的結(jié)構(gòu)來組織數(shù)據(jù)。
  • 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
    在分類器設(shè)計方面,介紹了利用神經(jīng)網(wǎng)絡(luò)進行模式識別的原理,采用前述的三種特征提取方法,分別結(jié)合判決樹分類器和神經(jīng)網(wǎng)絡(luò)分類器對信號進行分類,并且進行了試驗論證。
  • 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
    與其他分類算法相比，它能夠較快的建立簡單、易于理解的模型，容易轉(zhuǎn)換成規(guī)則，而且具有與其他分類模型同樣的，有時甚至更好的分類準確性。
  • 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
    本論文的組織結(jié)構(gòu)為：第一章為引言，作背景知識介紹，摘要闡述了數(shù)據(jù)挖掘在企業(yè)知識管理、泱策支持中的定位，以及數(shù)據(jù)挖掘的結(jié)構(gòu)、分類；第二章講述了分類數(shù)據(jù)挖掘的思路，重點講解了泱策樹分類器的構(gòu)建、修剪，第三章針對大規(guī)模數(shù)據(jù)對數(shù)據(jù)挖掘技術(shù)的影響做了講解，提出了可采取的相應(yīng)的處理手段，以及與關(guān)系數(shù)據(jù)庫、數(shù)據(jù)倉庫結(jié)合的問題；第四章給出了論文程序的框架、流程設(shè)計，以及幾個關(guān)鍵問題的設(shè)計；第五章對提出的設(shè)計進行簡要的評述，做論文總結(jié)，并對進一步的研究進行了規(guī)劃。
  • 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
    大型數(shù)據(jù)倉庫的處理速度問題目前是制約其推廣應(yīng)用的關(guān)鍵所在，也是這一領(lǐng)域的一個重要研究課題，也正是我們當前工作的重點：在前期研究工作的基礎(chǔ)上圍繞提高大型數(shù)據(jù)倉庫處理速度問題，建立改進的數(shù)據(jù)倉庫系統(tǒng)模型和相關(guān)算法，開發(fā)出面向中級以上企事業(yè)單位的、具有數(shù)據(jù)挖掘和分析能力的大型數(shù)據(jù)倉庫系統(tǒng)。建立大型數(shù)據(jù)倉庫所面臨的關(guān)鍵問題，是如何妥善解決實際業(yè)務(wù)數(shù)據(jù)的大規(guī)模、海量特征所帶來的處理速度和空間等問題，這也是當前挖掘技術(shù)研究必然面對的核心問題。本研究的目的是設(shè)計并實現(xiàn)大型數(shù)據(jù)倉庫系統(tǒng)中的分類數(shù)據(jù)挖掘工具? ?決策樹分類器，主要工作是在綜合了解現(xiàn)有決策樹分類算法的研究情況的前提下，對決策樹算法適應(yīng)大規(guī)模數(shù)據(jù)集的問題進行探討，力求設(shè)計出能較好地適應(yīng)大規(guī)模數(shù)據(jù)的分類器算法。
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