# American Institute of Mathematical Sciences

February  2019, 2(1): 1-9. doi: 10.3934/mfc.2019001

## Kernel-based online gradient descent using distributed approach

 Shantou University, No. 243 Daxue Rd., Shantou, Guangdong, China

* Corresponding author: Xiaming Chen

Published  March 2019

Fund Project: The first author is supported by STU Scientific Research Foundation for Talents grant (NTF-18022)

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.

Citation: Xiaming Chen. Kernel-based online gradient descent using distributed approach. Mathematical Foundations of Computing, 2019, 2 (1) : 1-9. doi: 10.3934/mfc.2019001
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