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ML
2000
ACM

MultiBoosting: A Technique for Combining Boosting and Wagging

13 years 5 months ago
MultiBoosting: A Technique for Combining Boosting and Wagging
MultiBoosting is an extension to the highly successful AdaBoost technique for forming decision committees. MultiBoosting can be viewed as combining AdaBoost with wagging. It is able to harness both AdaBoost's high bias and variance reduction with wagging's superior variance reduction. Using C4.5 as the base learning algorithm, Multi-boosting is demonstrated to produce decision committees with lower error than either AdaBoost or wagging significantly more often than the reverse over a large representative cross-section of UCI data sets. It offers the further advantage over AdaBoost of suiting parallel execution.
Geoffrey I. Webb
Added 19 Dec 2010
Updated 19 Dec 2010
Type Journal
Year 2000
Where ML
Authors Geoffrey I. Webb
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