Linganisha mbinu
Pitia mbinu ulizochagua bega kwa bega; safu zinazotofautiana zinaangaziwa.
| Kujifunza kwa Njia ya Mtandaoni Iliyodhibitiwa× | Kujifunza kwa uhamishaji× | |
|---|---|---|
| Nyanja | Ujifunzaji wa Mashine | Ujifunzaji wa Mashine |
| Familia | Machine learning | Machine learning |
| Mwaka wa asili≠ | 2007–2013 | 2010 (formalized); 1990s (early roots) |
| Mwanzilishi≠ | Xiao, L.; Shalev-Shwartz, S.; McMahan, H. B. et al. | Pan, S. J. & Yang, Q. (survey); Bengio, Y. (deep learning framing) |
| Aina≠ | Online optimization framework with regularization | Learning paradigm |
| Chanzo asilia≠ | Xiao, L. (2010). Dual Averaging Methods for Regularized Stochastic and Online Optimization. Journal of Machine Learning Research, 11, 2543–2596. link ↗ | 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 | FTRL, Follow-the-Regularized-Leader, online regularized optimization, regularized dual averaging | TL, domain adaptation, fine-tuning, pre-trained model adaptation |
| Zinazohusiana≠ | 6 | 3 |
| Muhtasari≠ | Regularized online learning extends the online learning paradigm by incorporating a regularization penalty into each weight update, controlling model complexity while processing data one example at a time. Algorithms such as Follow-the-Regularized-Leader (FTRL) and Regularized Dual Averaging (RDA) make this approach practical at scale, enabling sparse, well-calibrated models on streaming data. | 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. |
| ScholarGateSeti ya data ↗ |
|
|