Linganisha mbinu

	Autoencoder	Isomap	Support Vector Machine (Uainishaji)
Nyanja	Ujifunzaji wa Kina	Ujifunzaji wa Mashine	Ujifunzaji wa Mashine
Familia	Machine learning	Latent structure	Machine learning
Mwaka wa asili	2006	2000	1995
Mwanzilishi	Hinton, G.E. & Salakhutdinov, R.R.	Tenenbaum, J. B.; de Silva, V.; Langford, J. C.	Cortes, C. & Vapnik, V.
Aina	Neural network (encoder-decoder)	Manifold learning / nonlinear dimensionality reduction	Maximum-margin classifier (kernel method)
Chanzo asilia	Hinton, G.E. & Salakhutdinov, R.R. (2006). Reducing the Dimensionality of Data with Neural Networks. Science, 313(5786), 504–507.	Tenenbaum, J. B., de Silva, V. & Langford, J. C. (2000). A global geometric framework for nonlinear dimensionality reduction. Science, 290(5500), 2319–2323.	Cortes, C. & Vapnik, V. (1995). Support-Vector Networks. Machine Learning, 20, 273–297.
Majina mbadala	Otokodlayıcı (Autoencoder), otokodlayıcı, auto-encoder, encoder-decoder network	Isomap, isometric feature mapping, geodesic Isomap, nonlinear MDS	Destek Vektör Makinesi (SVM — Sınıflandırma), support-vector network, SVM classifier, maximum-margin classifier
Zinazohusiana	4	3	5
Muhtasari	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.	Isomap (Isometric Feature Mapping) is a manifold learning algorithm introduced by Tenenbaum, de Silva, and Langford in 2000 that discovers the intrinsic low-dimensional geometry of high-dimensional data by preserving geodesic — rather than straight-line Euclidean — distances between all pairs of points. It was one of the earliest, and most influential, nonlinear dimensionality reduction methods to demonstrate that genuinely curved data manifolds could be unfolded into a faithful low-dimensional coordinate system.	The Support Vector Machine, introduced by Corinna Cortes and Vladimir Vapnik in 1995, is a classifier that finds the optimal separating hyperplane between classes in a high-dimensional space. It chooses the boundary that leaves the widest possible margin to the nearest training points, which makes its decisions robust on new data.