dist.fun.bubble

The function is used as distance-dependent weight \(w\) for k-NN voting.

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

dist.fun.bubble function

<dl><dt>x</dt>
<dd>Distance or <code>numeric</code> vector or matrix of distances.</dd>
<dt>sigma</dt>
<dd>Maximum distance to be considered. Default is 1.1.</dd></dl>

Arguments

Bubble distance functions for topological k-NN classifier — dist.fun.bubble

<dl>

<dt>x</dt>
<dd>Distance or <code>numeric</code> vector or matrix of distances.</dd>


<dt>sigma</dt>
<dd>Maximum distance to be considered. Default is 1.1.</dd>

</dl>

dist.fun.bubble: Bubble distance functions for topological k-NN classifier

Description

Usage

Value

Arguments

Details