Description Usage Arguments References Examples
model from RSSL package WellSVM is a minimax relaxation of the mixed integer programming problem of finding the optimal labels for the unlabeled data in the SVM objective function. This implementation is a translation of the Matlab implementation of Li (2013) into R.
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C1 |
double; A regularization parameter for labeled data, default 1; |
C2 |
double; A regularization parameter for unlabeled data, default 0.1; |
gamma |
double; Gaussian kernel parameter, i.e., k(x,y) = exp(-gamma^2||x-y||^2/avg) where avg is the average distance among instances; when gamma = 0, linear kernel is used. default gamma = 1; |
x_center |
logical; Should the features be centered? |
scale |
logical; Should the features be normalized? (default: FALSE) |
use_Xu_for_scaling |
logical; whether the unlabeled objects should be used to determine the mean and scaling for the normalization |
max_iter |
integer; Maximum number of iterations |
Y.-F. Li, I. W. Tsang, J. T. Kwok, and Z.-H. Zhou. Scalable and Convex Weakly Labeled SVMs. Journal of Machine Learning Research, 2013.
R.-E. Fan, P.-H. Chen, and C.-J. Lin. Working set selection using second order information for training SVM. Journal of Machine Learning Research 6, 1889-1918, 2005.
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 | library(tidyverse)
library(tidymodels)
library(caret)
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 <- WellSVMSSLR() %>% fit(Class ~ ., data = train)
#Accesing model from RSSL
model <- m$model
#Accuracy
predict(m,test) %>%
bind_cols(test) %>%
metrics(truth = "Class", estimate = .pred_class)
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