split_dataset_ssl

Create Train, Test and Unlabeled Set

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

split_dataset_ssl function

<dl><dt>X</dt>
<dd>matrix; Design matrix</dd>
<dt>y</dt>
<dd>factor; Label vector</dd>
<dt>frac_train</dt>
<dd>numeric; Fraction of all objects to be used as training objects</dd>
<dt>frac_ssl</dt>
<dd>numeric; Fraction of training objects to used as unlabeled objects</dd></dl>

Arguments

Create Train, Test and Unlabeled Set — split_dataset_ssl

<dl>

<dt>X</dt>
<dd>matrix; Design matrix</dd>


<dt>y</dt>
<dd>factor; Label vector</dd>


<dt>frac_train</dt>
<dd>numeric; Fraction of all objects to be used as training objects</dd>


<dt>frac_ssl</dt>
<dd>numeric; Fraction of training objects to used as unlabeled objects</dd>

</dl>

split_dataset_ssl: Create Train, Test and Unlabeled Set

Description

Usage

Arguments

See Also