Plant identification via adaptive combination of transversal filters
Signal Processing - Signal processing in UWB communications
Competitive prediction under additive noise
IEEE Transactions on Signal Processing
Universal randomized switching
IEEE Transactions on Signal Processing
Improved adaptive filtering schemes via adaptive combination
Asilomar'09 Proceedings of the 43rd Asilomar conference on Signals, systems and computers
Factor graphs for universal portfolios
Asilomar'09 Proceedings of the 43rd Asilomar conference on Signals, systems and computers
Steady-state MSE performance analysis of mixture approaches to adaptive filtering
IEEE Transactions on Signal Processing
Transient and steady-state analysis of the affine combination of two adaptive filters
IEEE Transactions on Signal Processing
Unbiased model combinations for adaptive filtering
IEEE Transactions on Signal Processing
Voice quality management for IP networks based on automatic change detection of monitoring data
APNOMS'06 Proceedings of the 9th Asia-Pacific international conference on Network Operations and Management: management of Convergence Networks and Services
A novel adaptive diversity achieving channel estimation scheme for LTE
Proceedings of the first ACM international workshop on Practical issues and applications in next generation wireless networks
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A common problem that arises in adaptive filtering, autoregressive modeling, or linear prediction is the selection of an appropriate order for the underlying linear parametric model. We address this problem for linear prediction, but instead of fixing a specific model order, we develop a sequential prediction algorithm whose sequentially accumulated average squared prediction error for any bounded individual sequence is as good as the performance attainable by the best sequential linear predictor of order less than some M. This predictor is found by transforming linear prediction into a problem analogous to the sequential probability assignment problem from universal coding theory. The resulting universal predictor uses essentially a performance-weighted average of all predictors for model orders less than M. Efficient lattice filters are used to generate the predictions of all the models recursively, resulting in a complexity of the universal algorithm that is no larger than that of the largest model order. Examples of prediction performance are provided for autoregressive and speech data as well as an example of adaptive data equalization