পদ্ধতির তুলনা করুন

	সাপোর্ট ভেক্টর মেশিন (শ্রেণীকরণ)	সাপোর্ট ভেক্টর রিগ্রেশন
ক্ষেত্র	যন্ত্র শিখন	যন্ত্র শিখন
পরিবার	Machine learning	Machine learning
উদ্ভবের বছর	1995	2004
প্রবর্তক	Cortes, C. & Vapnik, V.	Smola, A.J. & Schölkopf, B.
ধরন	Maximum-margin classifier (kernel method)	Kernel-based supervised model (epsilon-insensitive regression)
মৌলিক উৎস	Cortes, C. & Vapnik, V. (1995). Support-Vector Networks. Machine Learning, 20, 273–297.	Smola, A.J. & Schölkopf, B. (2004). A Tutorial on Support Vector Regression. Statistics and Computing, 14, 199–222.
অপর নাম	Destek Vektör Makinesi (SVM — Sınıflandırma), support-vector network, SVM classifier, maximum-margin classifier	Destek Vektör Regresyonu (SVR), SVR, epsilon-SVR, support vector machine for regression
সম্পর্কিত	5	4
সারসংক্ষেপ	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.	Support Vector Regression (SVR), described in Smola and Schölkopf's 2004 tutorial, predicts a continuous outcome by fitting a function that stays within an epsilon-wide tube around the data while incurring as little error as possible. It extends the support vector machine idea from classification to regression, using a kernel to capture nonlinear relationships.