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conference paper
Diffusion LMS for multitask problems with overlapping hypothesis subspaces
2014
IEEE International Workshop on Machine Learning for Signal Processing (MLSP)
There are many important applications that are multitask-oriented in the sense that there are multiple optimum parameter vectors to be inferred simultaneously by networked agents. In this paper, we formulate an online multitask learning problem where node hypothesis spaces partly overlap. A cooperative algorithm based on diffusion adaptation is derived. Some results on its stability and convergence properties are also provided. Simulations are conducted to illustrate the theoretical results.
Type
conference paper
Authors
Publication date
2014
Publisher
Published in
IEEE International Workshop on Machine Learning for Signal Processing (MLSP)
Start page
1
End page
6
Peer reviewed
REVIEWED
EPFL units
Event name | Event place | Event date |
Reims, France | September 21-24, 2014 | |
Available on Infoscience
December 19, 2017
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