R: [experimental] Linear SVM classifier for texts

textmodel_svmlin {quanteda.textmodels} | R Documentation

## [experimental] Linear SVM classifier for texts

### Description

Fit a fast linear SVM classifier for sparse text matrices, using svmlin C++ code written by Vikas Sindhwani and S. Sathiya Keerthi. This method implements the modified finite Newton L2-SVM method (L2-SVM-MFN) method described in Sindhwani and Keerthi (2006). Currently, `textmodel_svmlin()` only works for two-class problems.

### Usage

    textmodel_svmlin(
      x,
      y,
      intercept = TRUE,
      lambda = 1,
      cp = 1,
      cn = 1,
      scale = FALSE,
      center = FALSE
    )

### Arguments

`x` | the dfm on which the model will be fit. Does not need to contain only the training documents.
`y` | vector of training labels associated with each document identified in `train`. (These will be converted to factors if not already factors.)
`intercept` | logical; if `TRUE`, add an intercept to the data
`lambda` | numeric; regularization parameter lambda (default 1)
`cp` | numeric; Relative cost for "positive" examples (the second factor level)
`cn` | numeric; Relative cost for "negative" examples (the first factor level)
`scale` | logical; if `TRUE`, normalize the feature counts
`center` | logical; if `TRUE`, centre the feature counts

### Value

a fitted model object of class `textmodel_svmlin`

### Warning

This function is marked experimental since it's not fully working yet in a way that translates into more standard SVM parameters that we understand. Use with caution after reading the Sindhwani and Keerthi (2006) paper.

### References

Vikas Sindhwani and S. Sathiya Keerthi (2006). Large Scale Semi-supervised Linear SVMs. Proceedings of ACM SIGIR. August 6–11, 2006, Seattle.

V. Sindhwani and S. Sathiya Keerthi (2006). Newton Methods for Fast Solution of Semi-supervised Linear SVMs. Book Chapter in Large Scale Kernel Machines, MIT Press, 2006.

### See Also

`predict.textmodel_svmlin()`

### Examples

    # use Lenihan for govt class and Bruton for opposition
    library("quanteda")
    docvars(data_corpus_irishbudget2010, "govtopp") <- c("Govt", "Opp", rep(NA, 12))
    dfmat <- dfm(tokens(data_corpus_irishbudget2010))

    tmod <- textmodel_svmlin(dfmat, y = dfmat$govtopp)
    predict(tmod)

[Package quanteda.textmodels version 0.9.4 Index]