Salīdzināt metodes
Apskatiet izvēlētās metodes blakus; rindas, kas atšķiras, ir izceltas.
| Regularizēta tiešsaistes apguve× | Daudzpusīgā apguve× | |
|---|---|---|
| Nozare | Mašīnmācīšanās | Mašīnmācīšanās |
| Saime | Machine learning | Machine learning |
| Izcelsmes gads≠ | 2007–2013 | 1970s–2006 (formalized) |
| Autors≠ | Xiao, L.; Shalev-Shwartz, S.; McMahan, H. B. et al. | Vapnik, V. N. and others (community of researchers, 1970s–2000s) |
| Tips≠ | Online optimization framework with regularization | Learning paradigm |
| Pirmavots≠ | Xiao, L. (2010). Dual Averaging Methods for Regularized Stochastic and Online Optimization. Journal of Machine Learning Research, 11, 2543–2596. link ↗ | Chapelle, O., Scholkopf, B., & Zien, A. (Eds.) (2006). Semi-Supervised Learning. MIT Press. ISBN: 978-0-262-03358-9 |
| Citi nosaukumi | FTRL, Follow-the-Regularized-Leader, online regularized optimization, regularized dual averaging | SSL, semi-supervised machine learning, transductive learning, label-efficient learning |
| Saistītās≠ | 6 | 5 |
| Kopsavilkums≠ | 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. | 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. |
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