Supervised, Unsupervised, and Semi-Supervised Learning: A Comparative Study for Real-World Adaptive Systems
DOI:
https://doi.org/10.63282/3050-9262.IJAIDSML-V7I1P154Keywords:
Supervised Learning, Unsupervised Learning, Semi-Supervised Learning, Adaptive Systems, Machine Learning Paradigms, Real-World Ai, Data-Driven SystemsAbstract
Machine learning systems increasingly operate in dynamic, data-rich environments where labeled data is limited, data distributions evolve over time, and decisions must adapt to changing conditions. While supervised learning has traditionally dominated applied machine learning, unsupervised and semi-supervised approaches have gained importance in real-world adaptive systems. This paper presents a comparative study of supervised, unsupervised, and semi-supervised learning paradigms, focusing on their applicability, strengths, and limitations in practical deployment scenarios. Rather than emphasizing mathematical formulations, the paper highlights operational considerations such as data availability, scalability, interpretability, and adaptability. Through real-world examples drawn from enterprise platforms, cloud systems, and intelligent applications, this study provides guidance on selecting appropriate learning paradigms for modern adaptive systems.
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