Abstract
An approach to the problem of linear prediction is discussed that is based on recent developments in the universal coding and computational learning theory literature. This development provides a novel perspective on the adaptive filtering problem, and represents a significant departure from traditional adaptive filtering methodologies. In this context, we demonstrate a sequential algorithm for linear prediction whose accumulated squared prediction error, for every possible sequence, is asymptotically as small as the best fixed linear predictor for that sequence.
Original language | English (US) |
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Pages (from-to) | 81 |
Number of pages | 1 |
Journal | IEEE International Symposium on Information Theory - Proceedings |
State | Published - 2000 |
Event | 2000 IEEE International Symposium on Information Theory - Serrento, Italy Duration: Jun 25 2000 → Jun 30 2000 |
ASJC Scopus subject areas
- Theoretical Computer Science
- Information Systems
- Modeling and Simulation
- Applied Mathematics