SSLR: LinearTSVMSSLR – R documentation

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LinearTSVMSSLR

General Interface for LinearTSVM model

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)

#Accuracy
predict(m,test) %>%
  bind_cols(test) %>%
  metrics(truth = "Class", estimate = .pred_class)

SSLR

Semi-Supervised Classification, Regression and Clustering Methods

v0.9.3.1

GPL-3

Authors

Francisco Jesús Palomares Alabarce [aut, cre] (<https://orcid.org/0000-0002-0499-7034>), José Manuel Benítez [ctb] (<https://orcid.org/0000-0002-2346-0793>), Isaac Triguero [ctb] (<https://orcid.org/0000-0002-0150-0651>), Christoph Bergmeir [ctb] (<https://orcid.org/0000-0002-3665-9021>), Mabel González [ctb] (<https://orcid.org/0000-0003-0152-444X>)

Initial release