论文标题
在学习相互作用粒子系统中的强制性条件下
On the coercivity condition in the learning of interacting particle systems
论文作者
论文摘要
在学习相互作用粒子或代理系统的系统中，强制性条件可确保相互作用功能的可识别性，从而通过非参数回归为学习的基础提供了基础。强制性条件等同于学习中不可或缺的内核的严格积极确定性。我们表明，对于一类相互作用的函数，系统是千古的，积分内核是严格的积极确定的，因此强制性条件是正确的。
In the learning of systems of interacting particles or agents, coercivity condition ensures identifiability of the interaction functions, providing the foundation of learning by nonparametric regression. The coercivity condition is equivalent to the strictly positive definiteness of an integral kernel arising in the learning. We show that for a class of interaction functions such that the system is ergodic, the integral kernel is strictly positive definite, and hence the coercivity condition holds true.
