Vertaile menetelmiä
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| AdaBoost× | Label Propagation× | Puoliohjattu oppiminen× | |
|---|---|---|---|
| Tieteenala | Koneoppiminen | Koneoppiminen | Koneoppiminen |
| Menetelmäperhe | Machine learning | Machine learning | Machine learning |
| Syntyvuosi≠ | 1997 | 2002 | 1970s–2006 (formalized) |
| Kehittäjä≠ | Freund, Y. & Schapire, R.E. | Zhu, X. & Ghahramani, Z. | Vapnik, V. N. and others (community of researchers, 1970s–2000s) |
| Tyyppi≠ | Ensemble (sequential boosting of weak learners) | Graph-based semi-supervised classification | Learning paradigm |
| Alkuperäislähde≠ | Freund, Y. & Schapire, R.E. (1997). A Decision-Theoretic Generalization of On-Line Learning and an Application to Boosting. Journal of Computer and System Sciences, 55(1), 119–139. DOI ↗ | Zhu, X., & Ghahramani, Z. (2002). Learning from labeled and unlabeled data with label propagation. Technical Report CMU-CALD-02-107, Carnegie Mellon University. link ↗ | Chapelle, O., Scholkopf, B., & Zien, A. (Eds.) (2006). Semi-Supervised Learning. MIT Press. ISBN: 978-0-262-03358-9 |
| Rinnakkaisnimet≠ | AdaBoost (Adaptive Boosting), adaptive boosting, adaptif artırma | LP, label spreading, graph-based semi-supervised learning, harmonic label propagation | SSL, semi-supervised machine learning, transductive learning, label-efficient learning |
| Liittyvät≠ | 5 | 3 | 5 |
| Tiivistelmä≠ | AdaBoost (Adaptive Boosting) is the original boosting algorithm, introduced by Yoav Freund and Robert Schapire in 1997, that combines a sequence of simple weak learners by giving more weight to the observations they get wrong. The forerunner of gradient boosting, it is simple, interpretable, and a strong baseline for classification. | Label Propagation is a graph-based semi-supervised learning algorithm introduced by Zhu and Ghahramani in 2002 that spreads class labels from a small set of labeled nodes to a large set of unlabeled nodes by iteratively diffusing label information along the edges of a similarity graph, exploiting the manifold structure of the 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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