tree n. 特里〔姓氏〕。 n. 1.樹〔主要指喬木,也可指較大的灌木〕。 ★玫瑰可以稱為 bush, 也可以稱為 tree. 2.木料,木材;木構(gòu)件;〔古語〕絞首臺(tái);〔the tree〕(釘死耶穌的)十字架;鞋楦。 3.樹形(物),世系圖,家系 (=family tree);【數(shù)學(xué)】樹(形);【化學(xué)】樹狀晶體。 a banana tree 香蕉樹。 an axle-tree 心棒,軸料。 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 菩提樹。 tree of heaven 臭椿。 tree of knowledge (of good and evil) 【圣經(jīng)】知道善惡的樹,智慧之樹。 tree of life 生命之樹,生命力的源泉【植物;植物學(xué)】金鐘柏。 up a tree 〔口語〕進(jìn)退兩難,不知所措。 vt. 趕(獵獸等)上樹躲避;〔口語〕使處于困境;窮追;把鞋型插入(鞋內(nèi))。
Bayesian - network based regression tree learning algorithm 基于貝葉斯網(wǎng)絡(luò)的回歸樹學(xué)習(xí)算法
Secret is a new algorithm for scalable linear regression trees Secret是一個(gè)關(guān)于可伸縮線形回歸樹的算法。
Improves on the classification and regression trees technology , increases it ' s classification precision 對(duì)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。
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。
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(dòng)客浙江大學(xué)碩士學(xué)位淪義綴戶識(shí)別系統(tǒng)。
The first part of this thesis describes the theory of rbf neural networks . the input space is thus divided into hyperrectangles organized into a regression tree ( binary tree ) by recursively partition the input space in two 第一部分從rbf網(wǎng)絡(luò)出發(fā),通過遞歸分割將輸入空間劃分為兩部分,從而將輸入空間變成一個(gè)用超矩形構(gòu)成的回歸樹(二叉樹) 。
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(dòng)客戶識(shí)別模型。
Chapter three is about credit risk measurement methodology such as zeta model , credit scoring - model , classification & regression tree , csfp model and credit metrics , the latest of which can measure the credit risk of abs / mbs dynamically 包括zeta法、資信評(píng)估模型、分類和回歸樹、 cspp開發(fā)的信用風(fēng)險(xiǎn)附加模型;動(dòng)態(tài)評(píng)估資產(chǎn)證券化的信用風(fēng)險(xiǎn)技術(shù)? ?信用風(fēng)險(xiǎn)計(jì)量法。
You can use multiple algorithms within one solution to perform separate tasks , for example by using a regression tree algorithm to obtain financial forecasting information , and a rule - based algorithm to perform a market basket analysis 可以在一個(gè)解決方案中使用多個(gè)算法來執(zhí)行不同的任務(wù),例如,使用回歸樹算法來獲取財(cái)務(wù)預(yù)測(cè)信息,使用基于規(guī)則的算法來執(zhí)行市場(chǎng)籃分析。
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(shí)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。