An Iterative Geometric Approach to Optimizing Separating Hyperplanes
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
Record details
Published: 19 July 2026
Source: arXiv cs.LG
Category: Research
Topics: Safety & alignment · Finance, VC & PE
Retrieved: 21 July 2026
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ethics.ai (19 July 2026), “An Iterative Geometric Approach to Optimizing Separating Hyperplanes,” evidence record 12269, https://ethics.ai/record/12269 (originally published by arXiv cs.LG).
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