Comparar métodos
Examine os métodos selecionados lado a lado; as linhas que diferem ficam destacadas.
| Online Gradient Boosting× | Gradient Boosting Semi-supervisionado× | |
|---|---|---|
| Área | Aprendizado de máquina | Aprendizado de máquina |
| Família | Machine learning | Machine learning |
| Ano de origem≠ | 2011–2015 | 2006–2010s |
| Autor original≠ | Grubb, A. & Bagnell, J. A.; Beygelzimer, A. et al. | Chapelle, Scholkopf & Zien (eds.); applied to GBM variants in subsequent literature |
| Tipo≠ | Online ensemble (sequential boosting on streaming data) | Semi-supervised ensemble (self-training + gradient boosted trees) |
| Fonte seminal≠ | Grubb, A. & Bagnell, J. A. (2011). Generalized Boosting Algorithms for Convex Optimization. Proceedings of the 28th International Conference on Machine Learning (ICML 2011), 1209–1216. link ↗ | Yarowsky, D. (1995). Unsupervised word sense disambiguation rivaling supervised methods. Proceedings of ACL 1995, 189–196. (Foundational self-training framework underlying pseudo-label approaches.) link ↗ |
| Outros nomes | OGB, streaming gradient boosting, incremental gradient boosting, online boosting with gradient descent | pseudo-label gradient boosting, self-training GBM, semi-supervised GBT, label-propagation boosting |
| Relacionados | 6 | 6 |
| Resumo≠ | Online Gradient Boosting adapts the gradient boosting framework for streaming settings where data arrives one sample at a time rather than as a fixed batch. At each step the model computes a pseudo-residual for the incoming observation and updates a weak learner in place, growing an additive ensemble without storing or revisiting past data. This makes it suitable for real-time prediction and large-scale streaming pipelines where retraining from scratch is infeasible. | Semi-supervised gradient boosting combines gradient boosted trees with self-training or pseudo-labeling to exploit large pools of unlabeled data alongside a small labeled set. An initial GBM fit on labeled data assigns confident predictions to unlabeled examples; those pseudo-labeled points are folded back into training and the model is re-boosted, iterating until convergence. This allows practitioners to harness cheap unlabeled data when labels are scarce or expensive. |
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