| 1. | Research and application of multivariate function extreme value
多元函数极值的研究与应用 |
| 2. | General methods of determining the type of extremum and constrained extremum of multivariate function
多元函数极值的二次型判定法 |
| 3. | We introduce and motivate the main theme of the course , the setting of the problem of learning from examples as the problem of approximating a multivariate function from sparse data - the examples
我们介绍且激发课程的主题将朝向于实例学习法的问题设定，例如稀疏值中多变量函数近似的问题。 |
| 4. | We look at the problem of learning from examples as the problem of multivariate function approximation from sparse chosen data , and then consider the case in which the data are drawn , instead of chosen , according to a probability measure
并检视稀疏精选值中多变量函数近似法等这些从实例学习法所发现的问题，然后根据机率衡量，审思随机获得资料而非选定资料的案例。 |
| 5. | By using conditional moment generating functions and differentiation of measures on a net , some limit theorems and a class of deviation theorems of multivariate function sequences of arbitrary random variables ralated to the conditional expectations are obtained
本论文利用条件矩母函数和网微分法，得到任意随机变量多元函数序列相对于条件期望的偏差定理和极限定理。 |
| 6. | A strong deviation theorem of multivariate function sequences of arbitrary random variables is obtained by using moment generating functions and differentiation of measures on a net . and a strong deviation theorem of discrete information sources is obtained
本论文利用母函数和网微分法得到任意随机变量多元函数序列的强偏差定理；及一个对离散信源普遍成立的强偏差定理。 |
