Linganisha mbinu
Pitia mbinu ulizochagua bega kwa bega; safu zinazotofautiana zinaangaziwa.
| Ujifunzaji Nusu-Simamiwa× | Kujifunza kwa uhamishaji× | |
|---|---|---|
| Nyanja | Ujifunzaji wa Mashine | Ujifunzaji wa Mashine |
| Familia | Machine learning | Machine learning |
| Mwaka wa asili≠ | 1970s–2006 (formalized) | 2010 (formalized); 1990s (early roots) |
| Mwanzilishi≠ | Vapnik, V. N. and others (community of researchers, 1970s–2000s) | Pan, S. J. & Yang, Q. (survey); Bengio, Y. (deep learning framing) |
| Aina | Learning paradigm | Learning paradigm |
| Chanzo asilia≠ | Chapelle, O., Scholkopf, B., & Zien, A. (Eds.) (2006). Semi-Supervised Learning. MIT Press. ISBN: 978-0-262-03358-9 | Pan, S. J., & Yang, Q. (2010). A Survey on Transfer Learning. IEEE Transactions on Knowledge and Data Engineering, 22(10), 1345–1359. DOI ↗ |
| Majina mbadala | SSL, semi-supervised machine learning, transductive learning, label-efficient learning | TL, domain adaptation, fine-tuning, pre-trained model adaptation |
| Zinazohusiana≠ | 5 | 3 |
| Muhtasari≠ | Semi-supervised learning (SSL) is a machine learning paradigm that trains models using a small set of labeled examples together with a much larger pool of unlabeled data. By leveraging the structure inherent in unlabeled data, SSL achieves accuracy closer to fully supervised models while requiring far fewer costly manual labels — making it practical when labeling is expensive, slow, or resource-constrained. | 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. |
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