kernlab is an extensible package for kernel-based machine learning methods in R. It takes advantage of R’s new S4 object model and provides a framework for creating and using kernel- based algorithms. Moreover it provides a general purpose quadratic programming solver, and an incomplete Cholesky decomposition method.

The plot function for binary classification ksvm objects displays a contour plot of the decision values with the corresponding support vectors highlighted. The predict function can return class probabilities for classification problems by setting the type parameter to “probabilities”.

A kernel is a list containing two mandatory elements and one optional element. The mandatory element ‘ kernel ‘ is the kernel function. It takes arguments y, x, h , where x is the data supplied to kdensity and y is the point of evaluation. h is the bandwidth.

e1071 is a package for R programming that provides functions for statistic and probabilistic algorithms like a fuzzy classifier, naive Bayes classifier, bagged clustering, short-time Fourier transform, support vector machine, etc.. When it comes to SVM, there are many packages available in R to implement it.

The caret package (short for Classification And REgression Training) is a set of functions that attempt to streamline the process for creating predictive models. The package contains tools for: data splitting. pre-processing.

Ksvm shows minimum number of support vectors than svm. The comparison of svm and ksvm was made for the twitter data set. The experimental results show that the number of support vectors by ksvm is very low when compare with normal svm.

xmatrix : Object of class “input” ( “list” for multiclass problems or “matrix” for binary classification and regression problems) containing the support vectors calculated from the data matrix used during computations (possibly scaled and without NA).

In R (for OSX) select Packages & Data -> Package Installer from the menu. If you haven’t already select a Datacenter. This is how I did it for mac: 1) Download the binary – https://CRAN.R-project.org/package=e1071 2) Run R CMD INSTALL e1071_version.

e1071 library
Creating SVM Model in R In order to create our SVM model, we will use the e1071 library and the iris dataset in R.

The caret symbol is written below the line of text for a line-level punctuation mark, such as a comma, or above the line as an inverted caret (cf. U+02C7 ˇ CARON) for a higher character, such as an apostrophe; the material to be inserted may be placed inside the caret, in the margin, or above the line.