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 either
hardor
soft-
kernel_functionis the kernel function to build the kernel matrix (not needed if
margin_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
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