Salīdzināt metodes
Apskatiet izvēlētās metodes blakus; rindas, kas atšķiras, ir izceltas.
| Daudzpusīgā apguve× | Balsošanas ansamblis× | |
|---|---|---|
| Nozare | Mašīnmācīšanās | Mašīnmācīšanās |
| Saime | Machine learning | Machine learning |
| Izcelsmes gads≠ | 1970s–2006 (formalized) | 1990s–2004 |
| Autors≠ | Vapnik, V. N. and others (community of researchers, 1970s–2000s) | Lam & Suen; Kuncheva, L. I. (systematic treatment) |
| Tips≠ | Learning paradigm | Ensemble (combination of multiple classifiers by vote) |
| Pirmavots≠ | Chapelle, O., Scholkopf, B., & Zien, A. (Eds.) (2006). Semi-Supervised Learning. MIT Press. ISBN: 978-0-262-03358-9 | Kuncheva, L. I. (2004). Combining Pattern Classifiers: Methods and Algorithms. Wiley-Interscience. ISBN: 978-0-471-21078-8 |
| Citi nosaukumi | SSL, semi-supervised machine learning, transductive learning, label-efficient learning | majority voting classifier, hard voting, soft voting ensemble, plurality voting ensemble |
| Saistītās | 5 | 5 |
| Kopsavilkums≠ | 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. | A voting ensemble trains several diverse classifiers independently and combines their predictions by a vote: hard voting picks the class chosen by the most models, while soft voting averages their class-probability estimates, optionally with per-model weights. The combination usually outperforms any individual member, and requires no additional training after the base models are fitted. |
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