可分数据上线性分类中的持续学习
Continual Learning in Linear Classification on Separable Data
AI总结：
本文研究可分二元线性分类任务序列上的持续学习，证明弱正则化等价于序贯最大间隔问题并归入POCS框架，给出循环与随机任务顺序下遗忘等指标的上界及训练启示。
摘要：
我们分析了一系列具有二元标签的可分线性分类任务上的持续学习。我们从理论上表明，使用弱正则化进行学习可归结为求解一个序贯最大间隔问题，这对应于凸集投影（Projection Onto Convex Sets，POCS）框架的一种特殊情形。随后，我们在任务重复出现的多种设置下（包括任务的循环排序和随机排序），给出了遗忘及其他相关量的上界。我们讨论了对正则化调度和加权等常用训练实践的若干实际启示。我们还指出了我们的持续分类设置与近期研究的持续回归设置之间的若干理论差异。
英文摘要：
We analyze continual learning on a sequence of separable linear classification tasks with binary labels. We show theoretically that learning with weak regularization reduces to solving a sequential max-margin problem, corresponding to a special case of the Projection Onto Convex Sets (POCS) framework. We then develop upper bounds on the forgetting and other quantities of interest under various settings with recurring tasks, including cyclic and random orderings of tasks. We discuss several practical implications to popular training practices like regularization scheduling and weighting. We point out several theoretical differences between our continual classification setting and a recently studied continual regression setting.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
