Linganisha mbinu

	Msitu Tenga Unaojifunza Wenyewe	Autoencoder
Nyanja	Ujifunzaji wa Mashine	Ujifunzaji wa Kina
Familia	Machine learning	Machine learning
Mwaka wa asili	2008–2020s	2006
Mwanzilishi	Liu, F. T., Ting, K. M., & Zhou, Z.-H. (iForest); SSL extensions by multiple authors	Hinton, G.E. & Salakhutdinov, R.R.
Aina	Ensemble anomaly detector with self-supervised pre-training	Neural network (encoder-decoder)
Chanzo asilia	Liu, F. T., Ting, K. M., & Zhou, Z.-H. (2008). Isolation Forest. In Proceedings of the 8th IEEE International Conference on Data Mining (ICDM), pp. 413–422.	Hinton, G.E. & Salakhutdinov, R.R. (2006). Reducing the Dimensionality of Data with Neural Networks. Science, 313(5786), 504–507.
Majina mbadala	SSL Isolation Forest, self-supervised iForest, semi-supervised isolation forest, contrastive isolation forest	Otokodlayıcı (Autoencoder), otokodlayıcı, auto-encoder, encoder-decoder network
Zinazohusiana	4	4
Muhtasari	Self-supervised Isolation Forest augments the classic Isolation Forest anomaly detector with a self-supervised pre-training stage. A pretext task — such as predicting rotation, masked features, or contrastive pairs — is solved without labels to learn a richer feature representation, which is then used when building the isolation trees, yielding sharper anomaly scores on complex, high-dimensional tabular data.	An autoencoder is an encoder-decoder neural network, popularised by Hinton and Salakhutdinov in 2006, that compresses data into a low-dimensional latent code and then reconstructs it, enabling dimensionality reduction and anomaly detection. By learning to rebuild its own input through a narrow bottleneck, it discovers a compact representation of the data.