เปรียบเทียบวิธี

	การเรียนรู้แบบออนไลน์	การเรียนรู้แบบกำกับดูแลตนเอง	การเรียนรู้แบบถ่ายโอน
สาขาวิชา	การเรียนรู้ของเครื่อง	การเรียนรู้ของเครื่อง	การเรียนรู้ของเครื่อง
ตระกูล	Machine learning	Machine learning	Machine learning
ปีกำเนิด	1958–2000s	2018–2020	2010 (formalized); 1990s (early roots)
ผู้ริเริ่ม	Rosenblatt, F.; Littlestone, N.; Shalev-Shwartz, S. (key contributors)	LeCun, Y. and community (formalized ~2018–2020)	Pan, S. J. & Yang, Q. (survey); Bengio, Y. (deep learning framing)
ประเภท	Learning paradigm (sequential model update)	Representation learning paradigm	Learning paradigm
แหล่งต้นตำรับ	Shalev-Shwartz, S. (2011). Online Learning and Online Convex Optimization. Foundations and Trends in Machine Learning, 4(2), 107–194.	LeCun, Y. & Misra, I. (2022). Self-supervised learning: The dark matter of intelligence. Meta AI Blog. https://ai.facebook.com/blog/self-supervised-learning-the-dark-matter-of-intelligence/	Pan, S. J., & Yang, Q. (2010). A Survey on Transfer Learning. IEEE Transactions on Knowledge and Data Engineering, 22(10), 1345–1359.
ชื่อเรียกอื่น	incremental learning, sequential learning, streaming learning, online machine learning	SSL, self-supervised pre-training, pretext-task learning, unsupervised representation learning	TL, domain adaptation, fine-tuning, pre-trained model adaptation
ที่เกี่ยวข้อง	6	3	3
สรุป	Online learning is a machine learning paradigm in which a model is updated incrementally as each new data point arrives, rather than being trained once on a fixed dataset. It is essential when data streams continuously, storage is limited, or the underlying distribution shifts over time. Theoretical performance is measured by cumulative regret relative to the best fixed predictor in hindsight.	Self-supervised learning (SSL) is a machine-learning paradigm that generates its own supervisory signal directly from unlabeled data by defining an auxiliary pretext task — such as predicting masked words, rotating images, or contrasting augmented views — and uses the learned representations as a powerful starting point for downstream tasks with minimal labeled examples.	Transfer learning is a machine learning paradigm in which knowledge gained from training a model on a source task or domain is reused to improve learning on a different but related target task or domain. It is especially powerful when labeled data for the target task is scarce, and it underlies most modern deep learning applications in computer vision, natural language processing, and beyond.