| 1. | Linear additive model
线性可加模型 |
| 2. | Methods we introduce the classical robust estimation to generalized additive models
方法将经典的稳健m估计方法引入广义可加模型。 |
| 3. | Objective to explore the application of generalized additive models in medical reserch
摘要目的探讨广义加性模型在医学研究领域中的应用。 |
| 4. | Strong consistency of the nearest neighbor estimates of low dimensional component in additive models
可加模型中低维分量近邻估计的强相合性 |
| 5. | Conclusion it is necessary to carry out robust estimation to generalized additive models when there are outliers in data
结论在离群点存在时对广义可加模型进行稳健估计是必要的。 |
| 6. | Firstly , an average performance index containing tracking error and control energy over a class of additive model errors is defined
首先针对一类相加模型误差的描述，定义了一个平均意义上的包含跟踪误差和控制能量的性能指标。 |
| 7. | The feedforward standard additive model is the most important special case of an additive fuzzy system and it is an important new framework for fuzzy systems
这种前向的标准可加性模糊系统是可加性系统中最重要的一种形式也是模糊系统的一个重要的新结构。 |
| 8. | Objective in studying the effects of air pollution on human health we use generalized additive models and diagnose the outliers then try to diminish the effect of them
摘要目的针对大气污染与健康关系研究中拟合广义可加模型时的离群点进行诊断并试图减小其影响。 |
| 9. | Methods it has been illustrated how to use generalized additive models and sas program by an example that is about the risk factor of the pregnancy induced hypertension syndrome
方法通过研究低出生体重与年龄、先兆流产、妊高症之间关系的实例分析说明模型的实际应用。 |
| 10. | In this paper , we introduce a new architecture , which stands for fuzzy neural network on the base of the standard additive model , and investigate some learning and adaptation strategies associated with the fuzzy sets
在本文中我们提出一种以标准可加性模型为基础，把模糊系统和神经网络相结合的新结构，并给出模糊集合学习和调整的新学习算法。 |
