Krahasoni metodat

	LightGBM i vetë-mbikëqyrur	Mësimi i Transferueshëm
Fusha	Mësimi i makinës	Mësimi i makinës
Familja	Machine learning	Machine learning
Viti i origjinës	2017–2020	2010 (formalized); 1990s (early roots)
Krijuesi	Ke, G. et al. (LightGBM); self-supervised paradigm adapted from broader SSL literature	Pan, S. J. & Yang, Q. (survey); Bengio, Y. (deep learning framing)
Lloji	Hybrid (self-supervised pretraining + gradient boosting)	Learning paradigm
Burimi themelues	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.
Emërtime të tjera	SSL-LightGBM, self-supervised gradient boosting, pretraining LightGBM, pseudo-label LightGBM	TL, domain adaptation, fine-tuning, pre-trained model adaptation
Të lidhura	6	3
Përmbledhja	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.