clapply

Use mclapply conditional on not being in RStudio

A collection of implementations of semi-supervised classifiers
and methods to evaluate their performance. The package includes implementations
of, among others, Implicitly Constrained Learning, Moment Constrained Learning,
the Transductive SVM, Manifold regularization, Maximum Contrastive Pessimistic
Likelihood estimation, S4VM and WellSVM.

Jesse Krijthe

RSSL

Implementations of Semi-Supervised Learning Approaches for
Classification

clapply function

<dl><dt>X</dt>
<dd>vector</dd>
<dt>FUN</dt>
<dd>function to be applied to the elements of X</dd>
<dt>...</dt>
<dd>optional arguments passed to FUN</dd>
<dt>mc.cores</dt>
<dd>number of cores to use</dd></dl>

Arguments

Use mclapply conditional on not being in RStudio — clapply

<dl>

<dt>X</dt>
<dd>vector</dd>


<dt>FUN</dt>
<dd>function to be applied to the elements of X</dd>


<dt>...</dt>
<dd>optional arguments passed to FUN</dd>


<dt>mc.cores</dt>
<dd>number of cores to use</dd>

</dl>

clapply: Use mclapply conditional on not being in RStudio

Description

Usage

Arguments