Linganisha mbinu

	Naive Bayes Inayojisimamia Yenyewe	Usalama-wa-kujitegemea wa Usawazishaji wa Usawa
Nyanja	Ujifunzaji wa Mashine	Ujifunzaji wa Mashine
Familia	Machine learning	Machine learning
Mwaka wa asili	2000	2020s
Mwanzilishi	Nigam, K.; McCallum, A. K.; Thrun, S.; Mitchell, T.	Chen et al. (SimCLR linear evaluation protocol, 2020); logistic probe practice widely adopted across SSL literature
Aina	Self-supervised generative classifier	Self-supervised pretraining + supervised linear classification
Chanzo asilia	Nigam, K., McCallum, A. K., Thrun, S., & Mitchell, T. (2000). Text classification from labeled and unlabeled documents using EM. Machine Learning, 39(2-3), 103–134.	Chen, T., Kornblith, S., Norouzi, M., & Hinton, G. (2020). A Simple Framework for Contrastive Learning of Visual Representations. Proceedings of the 37th International Conference on Machine Learning (ICML), 1597–1607.
Majina mbadala	Self-training Naive Bayes, EM Naive Bayes, Expectation-Maximization Naive Bayes, Pseudo-label Naive Bayes	SSL linear probe, contrastive pretraining with logistic classifier, self-supervised linear evaluation, SSL + logistic regression
Zinazohusiana	5	5
Muhtasari	Self-supervised Naive Bayes extends the classic Naive Bayes classifier to exploit large pools of unlabeled data by iteratively assigning soft pseudo-labels through an Expectation-Maximization loop. Originally demonstrated for text classification by Nigam et al. (2000), the approach can substantially improve accuracy when labeled examples are scarce but unlabeled data are plentiful.	Self-supervised logistic regression is a two-stage pipeline in which a neural encoder is first trained on abundant unlabeled data through a self-supervised pretext task — such as contrastive learning or masked prediction — and then the frozen learned representations are classified with a standard logistic regression model trained on a small labeled dataset. This linear evaluation protocol is widely used to benchmark the quality of self-supervised representations.