Kernel-based online gradient descent using distributed approach
Xiaming Chen · Mathematical Foundations of Computing · 2019
In this paper we study the kernel-based online gradient descent with least squares loss without an explicit regularization term. Our approach is novel by controlling the expectation of the K-norm of $ f_t $ using an iterative process. Then we use distributed learning to improve our result.