We design efficient on-line algorithms that predict nearly as well as the best pruning of a planar decision graph. We assume that the graph has no cycles. As in the previous work on decision trees, we implicitly maintain one weight for each of the prunings (exponentially many). The method works for a large class of algorithms that update its weights multiplicatively. It can also be used to design algorithms that predict nearly as well as the best convex combination of prunings.
CITATION STYLE
Takimoto, E., & Warmuth, M. K. (1999). Predicting nearly as well as the best pruning of a planar decision graph. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 1720, pp. 335–346). Springer Verlag. https://doi.org/10.1007/3-540-46769-6_28
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