This is a small package implementing soft and hard margin SVMs with abitrary kernels using the quadratic programming library quadprog
library(devtools)
install_github("alessio-b-zak/sv1-svm-example-package")
library(alessiosvm)
This package can be used by invoking the following function:
svm(X=X,
classes=classes,
C=C,
margin_type=margin_type,
kernel_function=kernel_function,
feature_map=feature_map)
where:
- X is a data matrix with observations on rows
- classes are the labels associated with each observation of the data matrix (either 1 or -1)
- C is the numeric cost associated with the soft margin classifier (not needed if margin_type == "hard")
-margin_typeis eitherhardorsoft-kernel_functionis the kernel function to build the kernel matrix (not needed ifmargin_type == "hard")
- feature_map is the map corresponding the the induced feature space of the kernel function (not needed if `margin_type == "hard")
svm() returns a model object which contains $prediction_function which classifies a new data point and $params which contains the w and b parameters associated with predition_function
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.