例句與造句

1. 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(jìn)行了改進(jìn)，使之具有更高的分類精度。
2. The thesis combines generalized computing theory with classification and regression trees technology , makes the great theory innovation
   本文把廣義計(jì)算理論和數(shù)據(jù)挖掘技術(shù)相結(jié)合，具有很強(qiáng)的理論創(chuàng)新意義。
3. 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(jì)算思想，把多準(zhǔn)則神經(jīng)網(wǎng)絡(luò)和分類回歸樹技術(shù)相結(jié)合，實(shí)現(xiàn)異動客浙江大學(xué)碩士學(xué)位淪義綴戶識別系統(tǒng)。
4. 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(jìn)行客戶關(guān)系管理，本文基于廣義計(jì)算思想，將多準(zhǔn)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。
5. 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(zhǔn)則神經(jīng)網(wǎng)絡(luò)和分類回歸樹各自的優(yōu)點(diǎn)，一定程度上避免各自的缺陷。
6. It's difficult to find classification and regression tree in a sentence. 用classification and regression tree造句挺難的
7. 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(zhǔn)則神經(jīng)網(wǎng)絡(luò)和決策樹的特點(diǎn)，論文提出了將多準(zhǔn)則神經(jīng)網(wǎng)絡(luò)應(yīng)用于決策樹的建模方法? ?基于多準(zhǔn)則神經(jīng)網(wǎng)絡(luò)和分類回歸樹的異動客戶識別系統(tǒng)。
8. 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(xiàng)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(jì)算的多準(zhǔn)則神經(jīng)網(wǎng)絡(luò)理論以及兩種理論的優(yōu)缺點(diǎn)。分析了多準(zhǔn)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。
9. 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(jì)算和分類回歸樹異動客戶識別系統(tǒng)這個項(xiàng)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。
10. 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(jià)?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(jì)之后，論文詳細(xì)介紹了該系統(tǒng)的開發(fā)，用以解決異動客戶的識別問題。