localDescent

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

localDescent function

<dl><dt>instance</dt>
<dd>Design matrix</dd>
<dt>label</dt>
<dd>label vector</dd>
<dt>labelNum</dt>
<dd>Number of labeled objects</dd>
<dt>unlabelNum</dt>
<dd>Number of unlabeled objects</dd>
<dt>gamma</dt>
<dd>Parameter for RBF kernel</dd>
<dt>C</dt>
<dd>cost parameter for SVM</dd>
<dt>beta</dt>
<dd>Controls fraction of objects assigned to positive class</dd>
<dt>alpha</dt>
<dd>Controls fraction of objects assigned to positive class</dd></dl>

Arguments

Local descent — localDescent

<dl>

<dt>instance</dt>
<dd>Design matrix</dd>


<dt>label</dt>
<dd>label vector</dd>


<dt>labelNum</dt>
<dd>Number of labeled objects</dd>


<dt>unlabelNum</dt>
<dd>Number of unlabeled objects</dd>


<dt>gamma</dt>
<dd>Parameter for RBF kernel</dd>


<dt>C</dt>
<dd>cost parameter for SVM</dd>


<dt>beta</dt>
<dd>Controls fraction of objects assigned to positive class</dd>


<dt>alpha</dt>
<dd>Controls fraction of objects assigned to positive class</dd>

</dl>

localDescent: Local descent

Description

Usage

Value

Arguments