In machine learning, support-vector machines (SVMs, also support-vector networks) are oversee teaching models with associated teaching algorithms that analyze data used for categorization and regression analysis. The support-vector clustering algorithm, created by Hava Siegelmann and Vladimir Vapnik, apply the statistics of support vectors, developed in the support vector machines algorithm, to categorize unlabeled data, and is one of the most widely used clustering algorithms in industrial applications. In 1992, Bernhard E. Boser, Isabelle M. Guyon and Vladimir N. Vapnik suggested a manner to make nonlinear classifiers by using the kernel trick to maximal-margin hyperplanes. The original SVM algorithm was invented by Vladimir N. Vapnik and Alexey Ya. The current standard incarnation (soft margin) was proposed by Corinna Cortes and Vapnik in 1993 and published in 1995.
library(e1071) x <- cbind(x_train,y_train) # Fitting model fit <-svm(y_train ~ ., data = x) summary(fit) # Predict Output predicted= predict(fit,x_test)