This paper focuses on learning rate analysis of distributed kernel ridge regression for strong mixing sequences. Using a recently developed integral operator approach and a classical covariance inequality for Banach-valued strong mixing sequences, we succeed in deriving optimal learning rate for distributed kernel ridge regression. As a byproduct, we also deduce a sufficient condition for the mixing property to guarantee the optimal learning rates for kernel ridge regression. Our results extend the applicable range of distributed learning from i.i.d. samples to non-i.i.d. sequences.

翻译：本文侧重于对分布式内核脊回归进行强烈混合序列的学习率分析。我们采用最近开发的综合操作员办法和对Banach估值强力混合序列的典型共变量不平等,成功地为分布式内核脊回归得出最佳学习率。作为一个副产品,我们还推断出混合财产的充足条件,以保证内核脊回归的最佳学习率。我们的结果扩大了从i.d.样本到非i.d.序列的可应用分布式学习范围。