Compara mètodes

	LightGBM auto-supervisat	Aprenentatge per transferència
Camp	Aprenentatge automàtic	Aprenentatge automàtic
Família	Machine learning	Machine learning
Any d'origen	2017–2020	2010 (formalized); 1990s (early roots)
Autor original	Ke, G. et al. (LightGBM); self-supervised paradigm adapted from broader SSL literature	Pan, S. J. & Yang, Q. (survey); Bengio, Y. (deep learning framing)
Tipus	Hybrid (self-supervised pretraining + gradient boosting)	Learning paradigm
Font seminal	Ke, G., Meng, Q., Finley, T., Wang, T., Chen, W., Ma, W., Ye, Q., & Liu, T.-Y. (2017). LightGBM: A Highly Efficient Gradient Boosting Decision Tree. Advances in Neural Information Processing Systems, 30.	Pan, S. J., & Yang, Q. (2010). A Survey on Transfer Learning. IEEE Transactions on Knowledge and Data Engineering, 22(10), 1345–1359.
Àlies	SSL-LightGBM, self-supervised gradient boosting, pretraining LightGBM, pseudo-label LightGBM	TL, domain adaptation, fine-tuning, pre-trained model adaptation
Relacionats	6	3
Resum	Self-supervised LightGBM combines the self-supervised learning paradigm with the LightGBM gradient boosting framework to exploit large volumes of unlabeled tabular data. A self-supervised pretext task — such as masked feature prediction or contrastive corruption — generates rich feature representations or pseudo-labels that are then used to train or fine-tune a LightGBM model, substantially improving performance in label-scarce regimes.	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.