قارن الطرق

	المُشَفِّر التلقائي	غابة العزل
المجال	التعلم العميق	تعلم الآلة
العائلة	Machine learning	Machine learning
سنة النشأة	2006	2008
صاحب الطريقة	Hinton, G.E. & Salakhutdinov, R.R.	Liu, F.T., Ting, K.M. & Zhou, Z.-H.
النوع	Neural network (encoder-decoder)	Unsupervised ensemble (random partitioning trees)
المصدر التأسيسي	Hinton, G.E. & Salakhutdinov, R.R. (2006). Reducing the Dimensionality of Data with Neural Networks. Science, 313(5786), 504–507.	Liu, F.T., Ting, K.M. & Zhou, Z.-H. (2008). Isolation Forest. IEEE ICDM, 413–422.
الأسماء البديلة	Otokodlayıcı (Autoencoder), otokodlayıcı, auto-encoder, encoder-decoder network	Isolation Forest (Aykırı Değer Tespiti), iForest, isolation forest anomaly detection
ذات صلة	4	5
الملخص	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.	Isolation Forest is an unsupervised machine-learning method for anomaly and outlier detection, introduced by Liu, Ting and Zhou in 2008, that isolates anomalies through random partitioning of the data. It works without any labelled anomaly data and scales to high-dimensional datasets.