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The classical approach to this problem is\nsimply maximization of the expected margin, while more recent proposals\nconsider simultaneous variance control and proxy objectives based on robust\nlocation estimates, in the vein of keeping the margin distribution sharply\nconcentrated in a desirable region. While conceptually appealing, these new\napproaches are often computationally unwieldy, and theoretical guarantees are\nlimited. Given this context, we propose an algorithm which searches the\nhypothesis space in such a way that a pre-set \"margin level\" ends up being a\ndistribution-robust estimator of the margin location. This procedure is easily\nimplemented using gradient descent, and admits finite-sample bounds on the\nexcess risk under unbounded inputs. Empirical tests on real-world benchmark\ndata reinforce the basic principles highlighted by the theory, and are\nsuggestive of a promising new technique for classification.","url_abs":"http://arxiv.org/abs/1810.04863v1","url_pdf":"http://arxiv.org/pdf/1810.04863v1.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"classification-using-margin-pursuit","repo_url":"https://github.com/feedbackward/catcube","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"none","reach":{"status":"ok","spdx":"MIT"}}],"tasks":[{"task_slug":"classification-1","task_name":"Classification"},{"task_slug":"classification","task_name":"General Classification"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":null,"atlas_url":"https://app.syntology.ai/?focus=1810.04863","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1810.04863"}},"developers":"https://syntology.ai/developers","read_at":"2026-09-24T18:15:14+00:00","read_at_is":"when the build read Syntology's graph, not when any sample ran","claim":"Per-sample execution status on synthesized fixtures; not a correctness claim about the paper. 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