{"paper":{"title":"Breakdown Point of Robust Support Vector Machine","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG"],"primary_cat":"stat.ML","authors_text":"Akiko Takeda, Shuhei Fujiwara, Takafumi Kanamori","submitted_at":"2014-09-03T01:39:34Z","abstract_excerpt":"The support vector machine (SVM) is one of the most successful learning methods for solving classification   problems. Despite its popularity, SVM has a serious drawback, that is sensitivity to outliers in training samples. The   penalty on misclassification is defined by a convex loss called the hinge loss, and the unboundedness of the convex   loss causes the sensitivity to outliers. To deal with outliers, robust variants of SVM have been proposed, such as the  robust outlier detection algorithm and an SVM with a bounded loss called the ramp loss. In this paper, we propose a  robust variant "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1409.0934","kind":"arxiv","version":1},"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"}