Learning r-of-k Functions by Boosting

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Learning r-of-k Functions by Boosting
We investigate further improvement of boosting in the case that the target concept belongs to the class of r-of-k threshold Boolean functions, which answer “+1” if at least r of k relevant variables are positive, and answer “−1” otherwise. Given m examples of a r-of-k function and literals as base hypotheses, popular boosting algorithms (e.g., AdaBoost) construct a consistent final hypothesis by using O(k2 log m) base hypotheses. While this convergence speed is tight in general, we show that a modification of AdaBoost (confidence-rated AdaBoost [SS99] or InfoBoost [Asl00]) can make use of the property of r-of-k functions that make less error on one-side to find a consistent final hypothesis by using O(kr log m) hypotheses. Our result extends the previous investigation by Hatano and Warmuth [HW04] and gives more general examples where confidence-rated AdaBoost or InfoBoost has an advantage over AdaBoost.
Kohei Hatano, Osamu Watanabe
Added 15 Mar 2010
Updated 15 Mar 2010
Type Conference
Year 2004
Where ALT
Authors Kohei Hatano, Osamu Watanabe
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