The nature of statistical learning theory
The nature of statistical learning theory
Learning in the presence of concept drift and hidden contexts
Machine Learning
Adaptive filter theory (3rd ed.)
Adaptive filter theory (3rd ed.)
The anatomy of a large-scale hypertextual Web search engine
WWW7 Proceedings of the seventh international conference on World Wide Web 7
Convex Optimization
Matrix Analysis For Scientists And Engineers
Matrix Analysis For Scientists And Engineers
Adaptive Filters
Dynamic Weighted Majority: An Ensemble Method for Drifting Concepts
The Journal of Machine Learning Research
LIBLINEAR: A Library for Large Linear Classification
The Journal of Machine Learning Research
Pattern Recognition, Fourth Edition
Pattern Recognition, Fourth Edition
Diffusion LMS strategies for distributed estimation
IEEE Transactions on Signal Processing
On classifying drifting concepts in P2P networks
ECML PKDD'10 Proceedings of the 2010 European conference on Machine learning and knowledge discovery in databases: Part I
Diffusion Least-Mean Squares Over Adaptive Networks: Formulation and Performance Analysis
IEEE Transactions on Signal Processing - Part II
Hi-index | 0.01 |
In this work, we analyze the generalization ability of distributed online learning algorithms under stationary and non-stationary environments. We derive bounds for the excess-risk attained by each node in a connected network of learners and study the performance advantage that diffusion has over individual non-cooperative processing. We conduct extensive simulations to illustrate the results.