LogisticRegressionFast

Logistic Regression implementation that uses R's glm

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

LogisticRegressionFast function

<dl><dt>X</dt>
<dd>matrix; Design matrix for labeled data</dd>
<dt>y</dt>
<dd>factor or integer vector; Label vector</dd>
<dt>lambda</dt>
<dd>numeric; not used</dd>
<dt>intercept</dt>
<dd>logical; Whether an intercept should be included</dd>
<dt>scale</dt>
<dd>logical; Should the features be normalized? (default: FALSE)</dd>
<dt>init</dt>
<dd>numeric; not used</dd>
<dt>x_center</dt>
<dd>logical; Should the features be centered?</dd></dl>

Arguments

Logistic Regression implementation that uses R's glm — LogisticRegressionFast

<dl>

<dt>X</dt>
<dd>matrix; Design matrix for labeled data</dd>


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


<dt>lambda</dt>
<dd>numeric; not used</dd>


<dt>intercept</dt>
<dd>logical; Whether an intercept should be included</dd>


<dt>scale</dt>
<dd>logical; Should the features be normalized? (default: FALSE)</dd>


<dt>init</dt>
<dd>numeric; not used</dd>


<dt>x_center</dt>
<dd>logical; Should the features be centered?</dd>

</dl>

LogisticRegressionFast: Logistic Regression implementation that uses R's glm

Description

Usage

Arguments