| 1. | The variable selection in covariance adjusted estimates
协方差改进估计中的变量选择 |
| 2. | ( 2 ) to put forward a four - dimension framework for variable choosing
( 2 )提出了四个维度变量选择框架。 |
| 3. | Covariance analysis with forward stepwise variable selection was carried out
统计方法采用逐步向前变量选择协方差分析。 |
| 4. | Covers choosing the most efficient data type for the control variable of a loop
介绍如何为循环的控制变量选择最有效的数据类型。 |
| 5. | Then , we define an input variable selection criterion to select and sequent candidate variables
定义了一种用于实现模糊模型的变量选择和排序的变量选择标准函数。 |
| 6. | Discusses how to choose an appropriate data type for a variable and the benefits of proper variable data type selection
讨论如何为变量选择适当的数据类型以及正确选择变量数据类型的益处。 |
| 7. | Finally , several problems for further research and exploration are proposed based on the summary of the research results
最后，在总结全文的基础上，探讨了变量选择技术有待进一步研究和探索的问题。 |
| 8. | Meanwhile , uniform incidence degree arithmetic can exactly obtain the correlation between variables , which is an effective way to select secondary variables
同时，一致关联度算法可以准确的提取变量间的相关性，是一种有效的辅助变量选择方法。 |
| 9. | The third chapter is the main body about the empirical analysis . firstly the paper clarifies the research thinking , choice variables , empirical equation and relationship
第三章是本文的主体部分，首先对研究思路、变量选择、实证方程和相关关系作了必要说明。 |
| 10. | A method of selecting mges that can be deleted is proposed . because too much mges " deletion will result in an ineffective model , mges can not be deleted totally at one time
提出一种线性稳态数据协调中的显著误差变量选择删除算法，以保证在显著误差变量个数过多的情况下原有模型的有效性。 |
