{"paper":{"title":"On the Doubt about Margin Explanation of Boosting","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.LG","authors_text":"Wei Gao, Zhi-Hua Zhou","submitted_at":"2010-09-19T07:26:37Z","abstract_excerpt":"Margin theory provides one of the most popular explanations to the success of \\texttt{AdaBoost}, where the central point lies in the recognition that \\textit{margin} is the key for characterizing the performance of \\texttt{AdaBoost}. This theory has been very influential, e.g., it has been used to argue that \\texttt{AdaBoost} usually does not overfit since it tends to enlarge the margin even after the training error reaches zero. Previously the \\textit{minimum margin bound} was established for \\texttt{AdaBoost}, however, \\cite{Breiman1999} pointed out that maximizing the minimum margin does no"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1009.3613","kind":"arxiv","version":5},"verdict":{"id":null,"model_set":{},"created_at":null,"strongest_claim":"","one_line_summary":"","pipeline_version":null,"weakest_assumption":"","pith_extraction_headline":""},"references":{"count":0,"sample":[],"resolved_work":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","internal_anchors":0},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"author_claims":{"count":0,"strong_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"builder_version":"pith-number-builder-2026-05-17-v1"}