欢迎您！东篱公司
退出
-
申报数据库
-
立项数据库
-
成果数据库
-
项目获奖数据库
中文摘要：
在ε-支持向量同归和粗糙v-支持向量回归模型的基础上，研究了新的粗糙ε-支持向量同归模型．利用同定对称边界粗糙ε-不敏感损失函数，得到粗糙ε-不敏感管，构造固定对称边界粗糙ε-支持向量回归模型；利用固定非对称边界粗糙-不敏感损失函数，得到粗糙ε^u-ε^d-不敏感管，构造固定非对称边界粗糙ε-支持向量回归模型．通过引进Lagrange函数和根据KKT条件，处理粗糙ε-支持向量回归模型的对偶问题．
英文摘要：
Two new rough e-support vector regression models are proposed based on ε-support vector regression, rough v-support vector regression and rough set theory. Firstly,a rough boundary ε-insensitive tube is defined with fixed symmetrical boundary rough ε-insensitive loss function, the method of optimization and ε-support vector regression model. Moreover we design a fixed symmetrical boundary rough ε-support vector regression model（RFSM SVR）. Secondly,we extend the model to the case that asymmetrical loss function is considered. Finally, according to Karush Kuhn Tucker（KKT）conditions, we derive their dual problems by introducing the Lagrange functional into rough ; support vector regression models.
同期刊论文项目
同项目期刊论文
Multi-confidence rule acquisition orientedattribute reduction of covering decision systemsvia combin
Attribute Reduction for Heterogeneous Data Based on the Combination of Classical and Fuzzy Rough Set
Evidence-theory-based numerical algorithms of attribute reduction with neighborhood-covering rough s
期刊信息
位置：