可变精度粗糙集模型 meaning in English
vprs
Examples
- 11 mounlinier i , ganascia j g . applying an existing machine learning algorithm to text categorization . in connectionist statistical , and symbolic approaches to learning for natural language processing , wermter s , riloff e , scheler g eds . , heidelberg , germany : springer verlag , lecture notes in computer science , vol
由于挖掘出的特征项目集可能很多，为了进一步的精简项目集，提出了一个以可变精度粗糙集模型为基础的方法对每个特征频繁项目集对分类的贡献进行评估，剪除那些对最后的分类效果贡献不大的项目集，并用精简后的项目集构造每类文档的主题模板。 - The basic rough set theory is introduced in brief . the method of how to get the decision rules through the rough set and recent popular arithmetic methods are mentioned . finally , a real - life example is given to explain the basic notions and get the decision rules to illustration the problem
3 .引入非参数式可变精度粗糙集模型,介绍一些基本的概念和性质,并给出证明;用分布一致性方法来对多属性决策问题进行多属性约简,引入相关的概念,并对所得到的性质和判定定理,给予理论上的证明,得出最后的决策步骤,并且最终获得多属性决策问题的决策规则。 - Among these rough set models , non - parameter variable precision rough set model ( nvprs ) , appeared to the improvement of variable precision rough set model ( vprs ) but really not , has been presented only for several years . this model , avoiding the choice of parameter , usually difficultly determined or not necessary used , could form the separated theory framework
在这些模型之中,非参数式可变精度粗糙集模型是这几年才提出来的,虽然它看似是可变精度粗糙集模型变型,但是却有自身的特点,避开了参数的选择的问题,而且自身可以形成一套适合自己的理论体系。