wlda

Implements weighted likelihood estimation for LDA

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

wlda function

<dl><dt>a</dt>
<dd>is the data set</dd>
<dt>w</dt>
<dd>is an indicator matrix for the K classes or, potentially, a weight matrix in which the fraction with which a sample belongs to a particular class is indicated</dd></dl>

Arguments

Implements weighted likelihood estimation for LDA — wlda

<dl>

<dt>a</dt>
<dd>is the data set</dd>


<dt>w</dt>
<dd>is an indicator matrix for the K classes or, potentially, a weight matrix in which the fraction with which a sample belongs to a particular class is indicated</dd>

</dl>

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wlda: Implements weighted likelihood estimation for LDA

Description

Usage

Value

Arguments

Join us for RADAR: AI Edition

Description

Usage

Value

Arguments

Join us for
RADAR: AI Edition