tree classification造句
例句與造句
- Decision tree classification algorithm based on bayesian method
基于貝葉斯方法的決策樹(shù)分類算法 - This paper is a study on decision tree classification algorithms , which mainly includes two parts
本文主要對(duì)決策樹(shù)分類算法展開(kāi)研究，主要包含兩個(gè)內(nèi)容： 1 - In the first part , two decision tree classification algorithms , sliq and sprint , is studied , because they are the most useful at present
研究了sliq算法和sprint算法。因?yàn)檫@兩個(gè)算法可以說(shuō)是目前決策樹(shù)算法中最有效的。 - Secondly , decision tree classification model and logistic regression model are performed to rock mass quality assessment , based on sas / enterprise miner
應(yīng)用sas enterpriseminer系統(tǒng)的決策樹(shù)分類算法和logistic回歸算法進(jìn)行巖體的質(zhì)量分級(jí)評(píng)價(jià)。 - A further study has been made about decision tree classification , bayesian network , and discretization of conntinuous attributes , at the same time many kinds of classfication algorithms have been achieved
對(duì)決策樹(shù)分類、貝葉斯網(wǎng)絡(luò)和連續(xù)屬性的離散化問(wèn)題進(jìn)行了的研究，實(shí)現(xiàn)了多種分類算法。 - It's difficult to find tree classification in a sentence. 用tree classification造句挺難的
- When we design the classification , we combine the tree classification and the support vector machines in order to improve the ability of combining experiences and performance of generalization
在模式識(shí)別的分類器設(shè)計(jì)上，我們采用了樹(shù)分類器和支持向量機(jī)相結(jié)合的方法，提高了分類器經(jīng)驗(yàn)結(jié)合的能力和泛化能力。 - We also make plenty of classification experiments with data sets from various of different fields , and then analyse and compare the classification capacity of several decision tree classification algorithms and the adaptability to different datas
在來(lái)自不同領(lǐng)域的數(shù)據(jù)集上進(jìn)行了大量的分類實(shí)驗(yàn)，分析和比較了多種決策樹(shù)分類算法的分類性能和對(duì)不同數(shù)據(jù)的適應(yīng)性。 - The algorithm of sf _ dt , which bases on the idea of decision tree classification algorithm ids , use the means of file splitting take the place of the means which bases on memory . it improves the scalability of classification algorithm and can deal with very large database
Sf _ dt算法以決策樹(shù)分類算法id3的基本思想為基礎(chǔ)，用基于文件分割的方法代替原有的基于內(nèi)存的算法，提高了算法的可規(guī)模性，可以處理超大規(guī)模的數(shù)據(jù)。 - In the data mining prototype system , apriori algorithm of association rules mining , id3 algorithm of decision tree classification , c4 . 5 pessimism estimate algorithm of decision tree classification and c4 . 5 reduced - error pruning algorithm of decision tree classification are realized
在數(shù)據(jù)挖掘原型系統(tǒng)中，實(shí)現(xiàn)了關(guān)聯(lián)分析的apriori算法、分類的id3決策樹(shù)算法、 c4 . 5的悲觀估計(jì)決策樹(shù)算法和c4 . 5決策樹(shù)的消除誤差修剪算法( reduced - errorpruning ) 。 - Tt _ dtc realizes a series of processes including data preprocess , decision tree classification , producing rules and prediction analysis , which based on the data of train tickets and aimed at the characters of tram tickets which have large amount of data and complex attributes
Tt _ dtc方法以鐵路客票數(shù)據(jù)為基礎(chǔ)，以鐵路客票營(yíng)銷分析為目的，針對(duì)鐵路客票信息數(shù)據(jù)量大、屬性復(fù)雜、域值廣等特點(diǎn)，實(shí)現(xiàn)了從數(shù)據(jù)預(yù)處理、決策樹(shù)生成到規(guī)則提取、知識(shí)產(chǎn)生等一系列過(guò)程。 - With rich data hi tram tickets system , how to mine useful knowledge is an important problem . applying the technology of classification hi train tickets analysis , we construct a new classification method tt _ dtc ( decision tree classification based on train tickets ) . we apply new classification algorithm sf _ dt ( decision tree classification algorithm based on splitting files ) that bases on splitting files and quantity rules , which aimed at the characters of train tickets
本文將數(shù)據(jù)挖掘中的分類技術(shù)用于鐵路客票營(yíng)銷分析中的客票分類，形成了一種新的分類方法tt _ dtc ( decisiontreeclassificationbasedontraintickets ) ，該方法針對(duì)鐵路客票的實(shí)際特點(diǎn)，采用新的基于文件分割和定量規(guī)則的決策樹(shù)分類算法sf _ dt ( decisiontreeclassificationalgorithmbasedonsplittingfiles )對(duì)客票數(shù)據(jù)進(jìn)行分析，以達(dá)到依據(jù)客票屬性特征對(duì)客票發(fā)售及列車運(yùn)營(yíng)情況進(jìn)行分類及預(yù)測(cè)的目的。