LinearTSVMSSLR: General Interface for LinearTSVM model

In SSLR: Semi-Supervised Classification, Regression and Clustering Methods

Description

model from RSSL package Implementation of the Linear Support Vector Classifier. Can be solved in the Dual formulation, which is equivalent to SVM or the Primal formulation.

Usage

LinearTSVMSSLR(
  C = 1,
  Cstar = 0.1,
  s = 0,
  x_center = FALSE,
  scale = FALSE,
  eps = 1e-06,
  verbose = FALSE,
  init = NULL
)

Arguments

C	Cost variable
Cstar	numeric; Cost parameter of the unlabeled objects
s	numeric; parameter controlling the loss function of the unlabeled objects
x_center	logical; Should the features be centered?
scale	Whether a z-transform should be applied (default: TRUE)
eps	Small value to ensure positive definiteness of the matrix in QP formulation
verbose	logical; Controls the verbosity of the output
init	numeric; Initial classifier parameters to start the convex concave procedure

Examples

library(tidyverse)
library(caret)
library(tidymodels)
library(SSLR)

data(breast)

set.seed(1)
train.index <- createDataPartition(breast$Class, p = .7, list = FALSE)
train <- breast[ train.index,]
test  <- breast[-train.index,]

cls <- which(colnames(breast) == "Class")

#% LABELED
labeled.index <- createDataPartition(breast$Class, p = .2, list = FALSE)
train[-labeled.index,cls] <- NA


m <- LinearTSVMSSLR() %>% fit(Class ~ ., data = train)


#Accesing model from RSSL
model <- m$model

SSLR documentation built on July 22, 2021, 9:08 a.m.