@inproceedings{Hu2007,
title = {Distributed density estimation using non-parametric statistics},
author = {Yusuo Hu and Hua Chen and Jian Guang Lou and Jiang Li},
booktitle = {27th International Conference on Distributed Computing Systems (ICDCS)},
url = {http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=4268183},
year = {2007},
abstract = {Learning the underlying model from distributed data is often useful
for many distributed systems. In this paper, we study the problem
of learning a non-parametric model from distributed observations.
We propose a gossip-based distributed kernel density estimation algorithm
and analyze the convergence and consistency of the estimation process.
Furthermore, we extend our algorithm to distributed systems under
communication and storage constraints by introducing a fast and efficient
data reduction algorithm. Experiments show that our algorithm can
estimate underlying density distribution accurately and robustly
with only small communication and storage overhead.},
timestamp = {2007.11.27}, owner = {Marco},
keywords = {DDM KDE }
}
::