Evaluating Kohonen's learning rule: An approach through genetic algorithms

Evaluating Kohonen's learning rule: An approach through genetic algorithms

0.00 Avg rating0 Votes
Article ID: iaor20051929
Country: Netherlands
Volume: 154
Issue: 1
Start Page Number: 191
End Page Number: 205
Publication Date: Apr 2004
Journal: European Journal of Operational Research
Authors: ,
Keywords: heuristics
Abstract:

This paper examines the technical foundations of the self-organising map (SOM). It compares Kohonen's heuristic-based training algorithm with direct optimisation of a locally-weighted distortion index, also used by Kohonen. Direct optimisation is achieved through a genetic algorithm (GA). Although GAs have been used before with the SOM, this has not been done in conjunction with the distortion index. Comparing heuristic-based training and direct optimisation for the SOM is analogous to comparing the Backpropagation algorithm for feedforward networks with direct optimisation of RMS error. Our experiments reveal lower values of the distortion index with direct optimisation. As to whether the heuristic-based algorithm is able to provide an approximation to gradient descent, our results suggest the answer should be in the negative. Theorems for one-dimensional and for square maps indicate that different point densities will emerge for the two training approaches. Our findings are in accordance with these results.

Reviews

Required fields are marked *. Your email address will not be published.