论文标题
分布式学习与依赖样本
Distributed Learning with Dependent Samples
论文作者
论文摘要
本文着重于针对强混合序列的分布式内核脊回归的学习率分析。使用最近开发的积分运算符方法和Banach值强的混合序列的经典协方差不平等，我们成功地得出了分布式内核脊回归的最佳学习率。作为副产品，我们还推导了混合特性的足够条件，以保证内核脊回归的最佳学习率。我们的结果将适用的分布式学习范围从I.I.D.样品到非i.i.d。序列。
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.
