nonlocal operators with integral kernels have become a popular tool for
designing solution maps between function spaces, due to their efficiency in
representing long-range dependence and the attractive feature of being
resolution-invariant. In this work, we provide a rigorous identifia
通过使用神经网络来近似再生核希尔伯特空间中的泛函的普适性,以及将其应用于广义函数线性模型的函数回归,本研究探讨了将功能性数据(如时间序列和图像)整合到神经网络中学习函数空间到 R 的映射(即泛函)的方法。同时,通过在再生核希尔伯特空间中建立内插正交投影,提出的网络简化了现有的功能学习工作,使用点评估替代基函数展开。