1) bidirectional approximate reasoning
双向近似推理
1.
Vague set-based bidirectional approximate reasoning;
基于Vague集的双向近似推理
2.
Based on these,a bidirectional approximate reasoning method on vague set by clustering is proposed.
在此基础上 ,提出了对 vague规则进行聚类 ,以及基于 vague聚类规则的双向近似推理方法 ,该方法更好地利用了 vague集信息的精确性 ,而且降低了推理的计算复杂性 ,从而提高了推理的精确性和适用性 。
3.
Based on the above, we present a bidirectional approximate reasoning method based on weighted similarity measures of vague sets, which fully uses the mes.
在此基础上给出了一种基于Vague集加权相似度量的双向近似推理方法 ,该方法更好地利用了Vague集信息的精确性 ,从而提高了推理的精确性和适用性。
2) approximate reasoning
近似推理
1.
An approximate reasoning approach based on the measures of similarity between vague sets;
基于Vague集的近似推理方法
2.
Equivalence of three different types of approximate reasoning patterns;
三种近似推理模式的等价性
3.
The approximate reasoning of Bayesian Network appending interval probability;
带区间参数的贝叶斯网的近似推理
3) approximate inference
近似推理
1.
Then the method for learning ID structures from interval data and the example of approximate inferences were proposed.
对传统影响图进行了扩充,提出了带区间参数的影响图(ID)概念,在此基础上,给出了基于遗传算法(GA)的带区间参数影响图的结构学习方法,采用Gibbs采样算法对该类影响图作了近似推理,并应用于具体实例。
2.
Bayesian networks have already had some relatively mature accurate and approximate inference algorithms as a result of twenty years development.
经过二十年的发展,贝叶斯网络已经有一些比较有效的精确和近似推理算法。
4) Bidirectional Approximation
双向近似
1.
Based on dynamic granulation principle,the concept of bidirectional approximation is proposed,some important properties are obtained.
基于动态粒度原理,提出双向近似的概念,获得双向近似的一些重要性质,并将其应用于决策表中决策规则的获取。
5) Quasiapproximate reasoni
准近似推理
6) Upper approximation reasoning
上近似推理
补充资料：演绎推理(见推理)
演绎推理(见推理)
deductive inference
住理。yanyl tullj演绎推理(deduetive infer。二ee)见
说明：补充资料仅用于学习参考，请勿用于其它任何用途。
参考词条