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Lecture Notes in Computer Science
We describe a new boosting algorithm that is the first such algorithm to be both smooth and adaptive. These two features make possible performance improvements for many learning tasks whose solutions use a boosting technique. The boosting approach was originally suggested for the standard PAC model; we analyze possible applications of boosting in the context of agnostic learning, which is more realistic than the PAC model. We derive a lower bound for the final error achievable by boosting indoi:10.1007/3-540-36169-3_10 fatcat:nu6xunw5jfhfxam3d32k36auka