计算机工程与应用
計算機工程與應用
계산궤공정여응용
Computer Engineering and Applications
2015年
22期
150-153
,共4页
支持向量机%不确定问题%模糊理论%粗糙集
支持嚮量機%不確定問題%模糊理論%粗糙集
지지향량궤%불학정문제%모호이론%조조집
support vector machine%uncertain problem%fuzzy theory%rough set
针对支持向量机方法处理不确定信息系统时存在的两个问题:一方面支持向量机训练对噪声样本敏感,另一方面支持向量机训练未考虑信息系统的不一致,利用模糊理论与粗糙集方法分别计算得到两种隶属度:模糊隶属度与粗糙隶属度,并将两种隶属度引入到标准支持向量机中得到一个新的支持向量机模型——双隶属度模糊粗糙支持向量机(DM-FRSVM)。分析该模型对于不确定问题的解决思路并进行对比研究,实验结果表明,在对于含有不确定信息的样本集进行分类时,DM-FRSVM表现出更好的推广性能。
針對支持嚮量機方法處理不確定信息繫統時存在的兩箇問題:一方麵支持嚮量機訓練對譟聲樣本敏感,另一方麵支持嚮量機訓練未攷慮信息繫統的不一緻,利用模糊理論與粗糙集方法分彆計算得到兩種隸屬度:模糊隸屬度與粗糙隸屬度,併將兩種隸屬度引入到標準支持嚮量機中得到一箇新的支持嚮量機模型——雙隸屬度模糊粗糙支持嚮量機(DM-FRSVM)。分析該模型對于不確定問題的解決思路併進行對比研究,實驗結果錶明,在對于含有不確定信息的樣本集進行分類時,DM-FRSVM錶現齣更好的推廣性能。
침대지지향량궤방법처리불학정신식계통시존재적량개문제:일방면지지향량궤훈련대조성양본민감,령일방면지지향량궤훈련미고필신식계통적불일치,이용모호이론여조조집방법분별계산득도량충대속도:모호대속도여조조대속도,병장량충대속도인입도표준지지향량궤중득도일개신적지지향량궤모형——쌍대속도모호조조지지향량궤(DM-FRSVM)。분석해모형대우불학정문제적해결사로병진행대비연구,실험결과표명,재대우함유불학정신식적양본집진행분류시,DM-FRSVM표현출경호적추엄성능。
It is difficult for support vector machine to deal with uncertain information because SVM is not only sensitive to noises and outliers but also the inconsistence between conditional features and decision labels is not taken into account. In order to overcome the problem, two types of membership are introduced into standard support vector machine, one type of membership is computed by the distance between the training samples and their center as fuzzy membership, the other type of membership is computed by the distance between the training samples and the nearest training sample with different class label as rough membership. At last several comparative experiments are made to show the performance and the validity of the proposed approach.