The machine learning formulation of online learning is incomplete from a
systems theoretic perspective. Typically, machine learning research emphasizes
domains and tasks, and a problem solving worldview. It focuses on algorithm
parameters, features, and samples, and neglects the perspective offered by
considering system structure and system behavior or dynamics. Online learning
is an active field of research and has been widely explored in terms of
statistical theory and computational algorithms, however, in general, the
literature still lacks formal system theoretical frameworks for modeling online
learning systems and resolving systems-related concept drift issues.
Furthermore, while the machine learning formulation serves to classify methods
and literature, the systems theoretic formulation presented herein serves to
provide a framework for the top-down design of online learning systems,
including a novel definition of online learning and the identification of key
design parameters. The framework is formulated in terms of input-output systems
and is further divided into system structure and system behavior. Concept drift
is a critical challenge faced in online learning, and this work formally
approaches it as part of the system behavior characteristics. Healthcare
provider fraud detection using machine learning is used as a case study
throughout the paper to ground the discussion in a real-world online learning
challenge.

机器学习在系统理论的视角下对在线学习的建模存在不完善之处，本文提出了一种基于系统设计的在线学习框架，包括在线学习的新定义和关键设计参数的识别，以及系统结构和系统行为的划分。同时，通过以医疗保健提供者欺诈检测为案例研究，将讨论与实际在线学习挑战相结合。