Universal Context Tree Least Squares Prediction

  title={Universal Context Tree Least Squares Prediction},
  author={A. C. Singer and S. S. Kozat},
  journal={2006 IEEE International Symposium on Information Theory},
We investigate the problem of sequential prediction of individual sequences using a competitive algorithm approach. We have previously developed prediction algorithms that are universal with respect to the class of all linear predictors, such that the prediction algorithm competes against a continuous class of prediction algorithms, under the square error loss. In this paper, we introduce the use of a "context tree," to compete against a doubly exponential number of piecewise linear models. We… CONTINUE READING

From This Paper

Figures, tables, and topics from this paper.

Explore Further: Topics Discussed in This Paper


Publications citing this paper.
Showing 1-3 of 3 extracted citations

Constrained Complexity Generalized Context-Tree Algorithms

2007 IEEE/SP 14th Workshop on Statistical Signal Processing • 2007
View 9 Excerpts
Highly Influenced

Universal Context Tree PTH-Order Least Squares Prediction

2006 16th IEEE Signal Processing Society Workshop on Machine Learning for Signal Processing • 2006
View 6 Excerpts


Publications referenced by this paper.
Showing 1-10 of 12 references

The context-tree weighting method: basic properties

IEEE Trans. Information Theory • 1995
View 4 Excerpts
Highly Influenced

Universal piecewise linear least squares prediction

International Symposium onInformation Theory, 2004. ISIT 2004. Proceedings. • 2004
View 1 Excerpt

Similar Papers

Loading similar papers…