Description Usage Arguments Value Author(s) References Examples

View source: R/self.train.kernel.R

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)
``` |

PPInfer documentation built on Nov. 1, 2018, 2:10 a.m.

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