TY - GEN
T1 - Diffusion adaptation over networks with kernel least-mean-square
AU - Gao, Wei
AU - Chen, Jie
AU - Richard, Cedric
AU - Huang, Jianguo
N1 - Publisher Copyright:
© 2015 IEEE.
PY - 2015
Y1 - 2015
N2 - Distributed learning over networks has become an active topic of research in the last decade. Adaptive networks are suitable for decentralized inference tasks, e.g., to monitor complex natural phenomena or infrastructure. Most of works focus on distributed estimation methods of linear regression models. However, there are many important applications that deal with nonlinear parametric models to be fitted, in a collaborative manner. In this paper, we derive functional diffusion strategies in reproducing kernel Hilbert spaces.
AB - Distributed learning over networks has become an active topic of research in the last decade. Adaptive networks are suitable for decentralized inference tasks, e.g., to monitor complex natural phenomena or infrastructure. Most of works focus on distributed estimation methods of linear regression models. However, there are many important applications that deal with nonlinear parametric models to be fitted, in a collaborative manner. In this paper, we derive functional diffusion strategies in reproducing kernel Hilbert spaces.
UR - https://www.scopus.com/pages/publications/84963808196
U2 - 10.1109/CAMSAP.2015.7383775
DO - 10.1109/CAMSAP.2015.7383775
M3 - 会议稿件
AN - SCOPUS:84963808196
T3 - 2015 IEEE 6th International Workshop on Computational Advances in Multi-Sensor Adaptive Processing, CAMSAP 2015
SP - 217
EP - 220
BT - 2015 IEEE 6th International Workshop on Computational Advances in Multi-Sensor Adaptive Processing, CAMSAP 2015
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 6th IEEE International Workshop on Computational Advances in Multi-Sensor Adaptive Processing, CAMSAP 2015
Y2 - 13 December 2015 through 16 December 2015
ER -