Magnification control in neural maps

Thomas Villmann, J. Michael Herrmann

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract / Description of output

In self-organising maps reconstruction error minimization and topology preservation have shown to be conflicting goals. For one dimensional maps this dilemma can be alleviated e.g. by locally adaptive learning rates. On the other hand, the neural gas algorithm and its topology representing extension allow for vector quantization at theoretically optimal reconstruction error for arbitrary data dimensionality . Thus, it is possible to modify the neural gas algorithm such as to meet optimality criteria other than mean square error in an exact way for data dimensions greater than one.
Original languageEnglish
Title of host publicationESANN'1998 proceedings - European Symposium on Artificial Neural Networks
Pages191-196
Number of pages6
Publication statusPublished - 1998

Fingerprint

Dive into the research topics of 'Magnification control in neural maps'. Together they form a unique fingerprint.

Cite this