Description Usage Arguments Value Author(s) References Examples
This function can be used for classification of semi-supervised data by using the kernel support vector machine.
1 2 3 | self.train.kernel(K, y, type = 'response', C = 1, cache = 40,
tol = 0.001, shrinking = TRUE, thrConf = 0.9,
maxIts = 10, percFull = 1, verbose = FALSE)
|
K |
kernel matrix |
y |
lable vector |
type |
one of response, probabilities ,votes, decision indicating the type of output (default: response) |
C |
cost of constraints violation for SVM (default: 1) |
cache |
cache memory in MB for SVM (default: 40) |
tol |
tolerance of termination criterion for SVM (default: 0.001) |
shrinking |
option whether to use the shrinking-heuristics for OCSVM (default: TRUE) |
thrConf |
A number between 0 and 1, indicating the required classification confidence for an unlabelled case to be added to the labelled data set with the label predicted predicted by the classification algorithm (default: 0.9) |
maxIts |
The maximum number of iterations of the self-training process (default: 10) |
percFull |
A number between 0 and 1. If the percentage of labelled cases reaches this value the self-training process is stoped (default: 1) |
verbose |
A boolean indicating the verbosity level of the function (default: FALSE) |
prediction from the SVM
Dongmin Jung, Xijin Ge
Torgo, L. (2016) Data Mining using R: learning with case studies, second edition, Chapman & Hall/CRC.
1 2 3 4 5 6 7 8 9 10 | data(litG)
litG <- igraph.from.graphNEL(litG)
sg <- decompose(litG, min.vertices = 50)
sg <- sg[[1]]
K <- net.kernel(sg)
y <- rep(NA, length(V(sg)))
y[1:10] <- 1
y[11:20] <- 0
y <- factor(y)
self.train.kernel(K, y)
|
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