计算机与网络
計算機與網絡
계산궤여망락
Computer & Network
2015年
20期
68-71
,共4页
优化相关向量机%云计算环境%冗余数据%分类
優化相關嚮量機%雲計算環境%冗餘數據%分類
우화상관향량궤%운계산배경%용여수거%분류
optimal relevance vector machine%cloud computing environment%redundant data%classification
传统算法无法避免云计算环境下冗余数据特征的动态变化性和特征多样性,从而降低了冗余数据分类的效果。为此,提出一种基于优化相关向量机的冗余数据分类方法。利用相关向量机建立冗余数据分类模型,并获得需要确定的参数,将参数看作粒子,构建初始粒子群,通过粒子群算法进行迭代寻优,获得最优分类模型的参数,从而实现云计算环境下冗余数据的准确分类。
傳統算法無法避免雲計算環境下冗餘數據特徵的動態變化性和特徵多樣性,從而降低瞭冗餘數據分類的效果。為此,提齣一種基于優化相關嚮量機的冗餘數據分類方法。利用相關嚮量機建立冗餘數據分類模型,併穫得需要確定的參數,將參數看作粒子,構建初始粒子群,通過粒子群算法進行迭代尋優,穫得最優分類模型的參數,從而實現雲計算環境下冗餘數據的準確分類。
전통산법무법피면운계산배경하용여수거특정적동태변화성화특정다양성,종이강저료용여수거분류적효과。위차,제출일충기우우화상관향량궤적용여수거분류방법。이용상관향량궤건립용여수거분류모형,병획득수요학정적삼수,장삼수간작입자,구건초시입자군,통과입자군산법진행질대심우,획득최우분류모형적삼수,종이실현운계산배경하용여수거적준학분류。
The traditional algorithms can't eliminate dynamic change and diversity of redundant data features in cloud computing environment, which reduces the redundant data classification effect. In view of this problem, this paper puts forward a kind of redundant data classification method based on optimal relevance vector machine. In this method, the relevance vector machine is used to establish redundant data classification model and obtain the parameters to be determined, and the determined parameters are taken as particles to build the initial particle swarm. The particle swarm optimization algorithm is used to implement iterative optimization and obtain the optimal classification model parameters, so as to realize accurate classification of redundant data in cloud computing environment.