השוואת שיטות

	Isomap	מכונת וקטורים תומכים (סיווג)
תחום	למידת מכונה	למידת מכונה
משפחה	Latent structure	Machine learning
שנת המקור	2000	1995
הוגה השיטה	Tenenbaum, J. B.; de Silva, V.; Langford, J. C.	Cortes, C. & Vapnik, V.
סוג	Manifold learning / nonlinear dimensionality reduction	Maximum-margin classifier (kernel method)
מקור מכונן	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.
כינויים	Isomap, isometric feature mapping, geodesic Isomap, nonlinear MDS	Destek Vektör Makinesi (SVM — Sınıflandırma), support-vector network, SVM classifier, maximum-margin classifier
קשורות	3	5
תקציר	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.