enrich.dirty

Maps x to the SOM defined in model and makes a list of dirty neurons
(i.e. neurons with more then one class label mapped).
All training samples in these neurons are added to the training set
to enhance their training.

internal

A topological version of k-NN: An abstract model is build
as 2-dimensional self-organising map. Samples of unknown
class are predicted by mapping them on the SOM and analysing
class membership of neurons in the neighbourhood.

Andreas Dominik

som.nn

Topological k-NN Classifier Based on Self-Organising Maps

enrich.dirty function

<dl><dt>x</dt>
<dd>training data</dd>
<dt>model</dt>
<dd>SOMnn model</dd>
<dt>multiple</dt>
<dd>enhancement factor for dirty samples</dd></dl>

Arguments

enrich training set with dirty mapped samples — enrich.dirty

<dl>

<dt>x</dt>
<dd>training data</dd>


<dt>model</dt>
<dd>SOMnn model</dd>


<dt>multiple</dt>
<dd>enhancement factor for dirty samples</dd>

</dl>

enrich.dirty: enrich training set with dirty mapped samples

Description

Usage

Arguments