{
  "id": 12269,
  "url": "https://arxiv.org/abs/2607.17282v1",
  "title": "An Iterative Geometric Approach to Optimizing Separating Hyperplanes",
  "summary": "Given a binary-labeled linearly separable dataset, and the objective is to compute the maximum-margin separating hyperplane, also known as the hard-margin Support Vector Machine (SVM) classifier. This paper investigates whether, if given an initial separating hyperplane, can it be exploited to reach this unique optimum more efficiently. We present a geometric approach that gradually improves the alignment of the hyperplane, starting from an initial separating hyperplane, while preserving separat",
  "authors": "Akos Hajnal",
  "category": "research",
  "topics": "safety-alignment,finance-investment",
  "orgs": null,
  "regions": null,
  "published_at": "2026-07-19T15:00:49.000Z",
  "fetched_at": "2026-07-21T05:10:12.656Z",
  "source_slug": "arxiv-cslg",
  "source_name": "arXiv cs.LG",
  "source_homepage": "https://arxiv.org/list/cs.LG/recent",
  "ethics_ai_record_url": "https://ethics.ai/record/12269",
  "original_url": "https://arxiv.org/abs/2607.17282v1",
  "evidence_status": "source-only",
  "attribution": "via ethics.ai"
}