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Table 1 SOM parameters together with allowed ranges.

From: Conformational and functional analysis of molecular dynamics trajectories by Self-Organising Maps

SOM parameter

Range

Map size

[100,400]

Lattice type

{hexagonal, rectangular}

Shape

{sheet, cylinder, toroid}

Learning algorithm

{batch, sequential}

Neighbour function

{gaussian, bubble, ep}

Alpha type

{inverse, linear, power}

Radius

{1, 2, 3}

Training length

[1000,5000]

Starting alpha

[0.01, 0.09]

  1. Map size (number of neurons), Lattice type, and Shape define the topological characteristics of the map. The other parameters are associated to the learning process: Learning algorithm, Neighbour function, Alpha type, Radius (neighbourhoods' kernel), Training length (number of learning cycles or epochs), Starting alpha (parameter defining the initial learning rate).
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BMC Bioinformatics

ISSN: 1471-2105

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