Predicting nearly as well as the best pruning of a planar decision graph
Source: Theoretical Computer Science Vol. 288, Issue 2, 17 September 2002, pp. 217 - 235.
Abstract. 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.*This work was done while the author visited University of California, Santa Cruz.
**Supported by NSF grant CCR 9821087.
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