Virginia Tech
A Systems Theoretic Framework for Online Machine Learning with an Empirical Application
Abstract
dc:description.abstractOnline (machine) learning is an active field of research which has been widely explored in terms of statistical learning theory, convex optimization theory and game theory, however, little to no frameworks exist for the design and application of online learning systems, both in theory and in practice. This work presents a unique, general framework for the modeling of online learning in general systems theoretic principles, which are not specific to any solution methods. Herein, online learning is defined as a system; its hierarchical relationship with machine learning is captured and deepened; its performance, properties and applications are re-defined in system theoretic terminology to discover alternative categorization and characterization of these systems; and its dynamic relationship with concept drift mathematically captured and explored. Subsequently, this work developed an unprecedented practical methodology to evaluate the testability of deployed online learning systems with – an unexplored, yet vital property for learning systems in real-world applications. In conclusion, this research developed an original systems theoretic framework and performance evaluation methodology for online learning to establish a foundation for the design, operation and analysis of online learning systems and their properties, in an effort to engineer safe and reliable real-world deployment of artificial intelligence.
Degree
thesis:*- Name thesis:degree_name
- Doctor of Philosophy
- Level thesis:degree_level
- doctoral
- Discipline thesis:degree_discipline
- Industrial and Systems Engineering
- Department dc:contributor.department
- Industrial and Systems Engineering
- Grantor dc:publisher
- Virginia Tech
- Year dc:date.issued
- 2025
Author and committee
dc:creator, dc:contributor.*- Author dc:creator
-
- du Preez, Anli
- Chairs dc:contributor.committeechair
-
- Beling, Peter A.
- Cody, Tyler Michael
- Committee members dc:contributor.committeemember
-
- Song, Binyang
- Tsui, Kwok
- Jin, Ran
Subjects
dc:subject × 3Rights
dc:rights- Statement dc:rights
-
- In Copyright
- Licence dc:rights.uri
- Language dc:language.iso
- en
Identifiers
dc:identifier.*- Dc Identifier Other
- vt_gsexam:43831
- OAI identifier oai:identifier
- oai:vtechworks.lib.vt.edu:10919/136964