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classification and regression tree in Chinese

Pronunciation:
How to pronounce "classification and regression tree""classification and regression tree" in a sentence

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  • 分類和回歸樹

Examples

  • Improves on the classification and regression trees technology , increases it ' s classification precision
    對分類回歸樹數(shù)據(jù)挖掘技術(shù)進行了改進，使之具有更高的分類精度。
  • The thesis combines generalized computing theory with classification and regression trees technology , makes the great theory innovation
    本文把廣義計算理論和數(shù)據(jù)挖掘技術(shù)相結(jié)合，具有很強的理論創(chuàng)新意義。
  • Combines multi - rules neural network with classification and regression trees technology based on generalized computing theory , implements the abnormal customers recognition system
    基于廣義計算思想，把多準則神經(jīng)網(wǎng)絡(luò)和分類回歸樹技術(shù)相結(jié)合，實現(xiàn)異動客浙江大學(xué)碩士學(xué)位淪義綴戶識別系統(tǒng)。
  • Based on the generalized computing theory , the thesis combines multi - rules neural network with a kind of decision tree - classification and regression trees . further more , we put forward a new kind of abnormal customers recognition model
    為進行客戶關(guān)系管理，本文基于廣義計算思想，將多準則神經(jīng)網(wǎng)絡(luò)和一種決策樹? ?分類回歸樹相結(jié)合，提出了一種新的異動客戶識別模型。
  • The model can improve classification precision and recognition efficiency effectively , make full use of the advantages of multi - rules neural network and classification and regression trees , and make up their respective disadvantages at a certain extent
    該模型能夠有效提高分類精度和識別效率，充分利用多準則神經(jīng)網(wǎng)絡(luò)和分類回歸樹各自的優(yōu)點，一定程度上避免各自的缺陷。
  • And then , the thesis brings forward a new modeling method - abnormal customers recognition system based on generalized computing and classification and regression trees . the system is composed of multi - rules neural network learning part and classification and regression trees processing part
    然后，通過深入研究多準則神經(jīng)網(wǎng)絡(luò)和決策樹的特點，論文提出了將多準則神經(jīng)網(wǎng)絡(luò)應(yīng)用于決策樹的建模方法? ?基于多準則神經(jīng)網(wǎng)絡(luò)和分類回歸樹的異動客戶識別系統(tǒng)。
  • The work that is carried out by me for this project as follows : at first , works over the decision tree technology and the multi - rules neural network theory based on the generalized computing , outlines the advantages and disadvantages of the two theories , analyzes the possibility to combine multi - rules neural network with classification and regression trees , and studies some achievement in this field
    為完成這個項目，本人所做的工作具體如下：首先研究了數(shù)據(jù)挖掘技術(shù)中的決策樹技術(shù)和基于廣義計算的多準則神經(jīng)網(wǎng)絡(luò)理論以及兩種理論的優(yōu)缺點。分析了多準則神經(jīng)網(wǎng)絡(luò)和決策樹相結(jié)合的可能性及優(yōu)勢，并深入了解目前該方向的發(fā)展情況。
  • Along with the rapid development of the technology of data warehouse and data mining , customer relationship management ( crm ) becomes more and more important . on this need , we advanced the project of abnormal customers recognition system based on generalized computing and classification and regression trees ( cart )
    基于廣義計算和分類回歸樹異動客戶識別系統(tǒng)這個項目，是在數(shù)據(jù)倉庫技術(shù)和數(shù)據(jù)挖掘技術(shù)迅速發(fā)展的基礎(chǔ)上，針對企業(yè)客戶關(guān)系管理的迫切需要而提出的。
  • Classification and regression trees processing part introduces growing algorithm of cart , pruning algorithm of cart and selecting best tree algorithm etc . on the basis of the concerned new model , the thesis presents in details the designing of multi - rules neural network based cart system for abnormal customers recognition
    在分類回歸樹部分，介紹了分類回歸樹的生長算法、最小代價?復(fù)雜性剪枝算法以及最優(yōu)樹選擇等算法。提出了系統(tǒng)設(shè)計之后，論文詳細介紹了該系統(tǒng)的開發(fā)，用以解決異動客戶的識別問題。

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