Mistake bounds and logarithmic linear-threshold learning algorithms
Mistake bounds and logarithmic linear-threshold learning algorithms
The power of amnesia: learning probabilistic automata with variable memory length
Machine Learning - Special issue on COLT '94
Exponentiated gradient versus gradient descent for linear predictors
Information and Computation
Predicting Nearly As Well As the Best Pruning of a Decision Tree
Machine Learning - Special issue on the eighth annual conference on computational learning theory, (COLT '95)
A Fast, Bottom-Up Decision Tree Pruning Algorithm with Near-Optimal Generalization
ICML '98 Proceedings of the Fifteenth International Conference on Machine Learning
On the algorithmic implementation of multiclass kernel-based vector machines
The Journal of Machine Learning Research
The context-tree weighting method: basic properties
IEEE Transactions on Information Theory
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Prediction suffix trees (PSTs) are a popular tool for modeling sequences and have been successfully applied in many domains such as compression and language modeling. In this work we adapt the well studied Winnow algorithm to the task of learning PSTs. The proposed algorithm automatically grows the tree, so that it provably remains competitive with any fixed PST determined in hindsight. At the same time we prove that the depth of the tree grows only logarithmically with the number of mistakes made by the algorithm. Finally, we empirically demonstrate its effectiveness in two different tasks.