predict.LiblineaR.ACF

Object of class <code>"LiblineaR.ACF"</code>, created by
<code>LiblineaR.ACF</code>.

object

An n x p matrix containing the new input data. A vector will be
transformed to a n x 1 matrix. A sparse matrix (from SparseM package) will
also work.

newx

Logical indicating whether model decision values should
be computed and returned. Default is <code>FALSE</code>.

decisionValues


The function applies a classification model produced by the <code>LiblineaR.ACF</code> function to every row of a
data matrix and returns the model predictions.


multivariate

classes

models

classif

optimize

Solving the linear SVM problem with coordinate descent
is very efficient and is implemented in one of the most often used packages,
'LIBLINEAR' (available at http://www.csie.ntu.edu.tw/~cjlin/liblinear).
It has been shown that the uniform selection of coordinates can be
accelerated by using an online adaptation of coordinate frequencies (ACF).
This package implements ACF and is based on 'LIBLINEAR' as well as
the 'LiblineaR' package (<https://cran.r-project.org/package=LiblineaR>).
It currently supports L2-regularized L1-loss as well as L2-loss linear SVM.
Similar to 'LIBLINEAR' multi-class classification (one-vs-the rest, and
Crammer & Singer method) and cross validation for model selection is
supported. The training of the models based on ACF is much faster than
standard 'LIBLINEAR' on many problems.

predict.LiblineaR.ACF: Predictions with LiblineaR.ACF model

Description

Usage

Arguments

Value

References

See Also