Online Learning under Haphazard Input Conditions: A Comprehensive Review and Analysis

arXiv:2404.04903v1 Announce Type: new
Abstract: The domain of online learning has experienced multifaceted expansion owing to its prevalence in real-life applications. Nonetheless, this progression operates under the assumption that the input feature space of the streaming data remains constant. In this survey paper, we address the topic of online learning in the context of haphazard inputs, explicitly foregoing such an assumption. We discuss, classify, evaluate, and compare the methodologies that are adept at modeling haphazard inputs, additionally providing the corresponding …

analysis and analysis arxiv cs.ai cs.lg online learning review type