Distributed Kernel Regression: An Algorithm for Training Collaboratively
机器学习
2016-11-15 v1 人工智能
分布式、并行与集群计算
信息论
math.IT
摘要
This paper addresses the problem of distributed learning under communication constraints, motivated by distributed signal processing in wireless sensor networks and data mining with distributed databases. After formalizing a general model for distributed learning, an algorithm for collaboratively training regularized kernel least-squares regression estimators is derived. Noting that the algorithm can be viewed as an application of successive orthogonal projection algorithms, its convergence properties are investigated and the statistical behavior of the estimator is discussed in a simplified theoretical setting.
引用
@article{arxiv.cs/0601089,
title = {Distributed Kernel Regression: An Algorithm for Training Collaboratively},
author = {Joel B. Predd and Sanjeev R. Kulkarni and H. Vincent Poor},
journal= {arXiv preprint arXiv:cs/0601089},
year = {2016}
}
备注
To be presented at the 2006 IEEE Information Theory Workshop, Punta del Este, Uruguay, March 13-17, 2006