在线随机学习器的跟踪性能
Tracking Performance of Online Stochastic Learners
AI总结：
该研究建立平稳假设下在线随机学习器的稳态性能与随机游走模型下跟踪性能的关联，可直接从稳态表达式推断跟踪性能，为大规模漂移场景下的算法性能分析提供新方法。
摘要：
在线随机算法因能够实时计算更新、无需存储和处理大批量数据，在大规模学习场景中得到广泛应用。当采用恒定步长时，这类算法还能适应问题参数（如数据或模型属性）的漂移，并以合理的精度跟踪最优解。本文借助与自适应滤波器研究的类比，建立了平稳性假设下推导的稳态性能与随机游走模型下在线学习器跟踪性能之间的关联。这一关联使得我们几乎可通过直观观察，直接从稳态表达式推断出跟踪性能。
英文摘要：
The utilization of online stochastic algorithms is popular in large-scale learning settings due to their ability to compute updates on the fly, without the need to store and process data in large batches. When a constant step-size is used, these algorithms also have the ability to adapt to drifts in problem parameters, such as data or model properties, and track the optimal solution with reasonable accuracy. Building on analogies with the study of adaptive filters, we establish a link between steady-state performance derived under stationarity assumptions and the tracking performance of online learners under random walk models. The link allows us to infer the tracking performance from steady-state expressions directly and almost by inspection.
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。
