计算机工程与应用
計算機工程與應用
계산궤공정여응용
COMPUTER ENGINEERING AND APPLICATIONS
2010年
4期
132-133,141
,共3页
属性加权%朴素贝叶斯%分类模型%相关性度量
屬性加權%樸素貝葉斯%分類模型%相關性度量
속성가권%박소패협사%분류모형%상관성도량
attribute weighted%naive Bayesian%classification model%relevant measure
构造了一种新的属性间相关性度量方法,提出了改进属性加权的朴素贝叶斯分类模型.经实验证明,提出的朴素贝叶斯分类模型明显优于张舜仲等人提出的分类模型.
構造瞭一種新的屬性間相關性度量方法,提齣瞭改進屬性加權的樸素貝葉斯分類模型.經實驗證明,提齣的樸素貝葉斯分類模型明顯優于張舜仲等人提齣的分類模型.
구조료일충신적속성간상관성도량방법,제출료개진속성가권적박소패협사분류모형.경실험증명,제출적박소패협사분류모형명현우우장순중등인제출적분류모형.
To improve attribute weighted the naive Bayesian classifier model,a new measurement method of the inter-related weighted attributes is structured.The experiment proves that the naive Bayesian classifier model is superior to the classification model proposed by Zhang Shun-zhong et al.
