吉首大学学报（自然科学版）2026，Vol.47Issue(4)：11-18,8.DOI:10.13438/j.cnki.jdzk.2026.04.003
基于一致遍历马氏链样本的分布式Huber回归算法的泛化性
Generalization of Distributed Huber Regression Algorithms with Uniformly Ergodic Markov Chain Samples
摘要
Abstract
This paper investigates the generalization performance of distributed Huber regression with samples generated from uniformly ergodic Markov chains.We first construct a distributed Huber regres-sion algorithm applicable to uniformly ergodic Markov chain samples.Relying on statistical learning the-ory,we further derive the convergence rates and generalization bounds for the proposed distributed Hu-ber regression with Markov chain sampling.Theoretical proofs demonstrate that the distributed Huber regression algorithm achieves a fast convergence rate even in the non-independent and identically distrib-uted(non-i.i.d.)sample setting.关键词
分布式/Huber回归算法/一致遍历马氏链样本/收敛速率Key words
distributed/huber regression algorithm/uniformly ergodic Markov chain samples/convergence rates分类
生物科学引用本文复制引用
李珂,姜宏伟..基于一致遍历马氏链样本的分布式Huber回归算法的泛化性[J].吉首大学学报（自然科学版）,2026,47(4):11-18,8.基金项目
国家自然科学基金资助项目(42171351) （42171351）
