Winner-relaxing and winner-enhancing Kohonen maps: Maximal mutual information from enhancing the winner
| dc.creator | Claussen, Jens Christian | |
| dc.date | 2006-09-20 | |
| dc.date.accessioned | 2026-07-07T07:23:38Z | |
| dc.date.available | 2026-07-07T07:23:38Z | |
| dc.description | The magnification behaviour of a generalized family of self-organizing feature maps, the Winner Relaxing and Winner Enhancing Kohonen algorithms is analyzed by the magnification law in the one-dimensional case, which can be obtained analytically. The Winner-Enhancing case allows to acheive a magnification exponent of one and therefore provides optimal mapping in the sense of information theory. A numerical verification of the magnification law is included, and the ordering behaviour is analyzed. Compared to the original Self-Organizing Map and some other approaches, the generalized Winner Enforcing Algorithm requires minimal extra computations per learning step and is conveniently easy to implement. | |
| dc.description | 6 pages, 5 figures. For an extended version refer to cond-mat/0208414 (Neural Computation 17, 996-1009) | |
| dc.identifier | https://arxiv.org/abs/cond-mat/0609513 | |
| dc.identifier | http://arxiv.org/abs/cond-mat/0609513 | |
| dc.identifier | Complexity 8(4), 15-22 (2003) | |
| dc.identifier | doi:10.1002/cplx.10084 | |
| dc.identifier.uri | http://salesiana.dossiersoluciones.com/handle/123456789/116053 | |
| dc.subject | Statistical Mechanics | |
| dc.subject | Disordered Systems and Neural Networks | |
| dc.title | Winner-relaxing and winner-enhancing Kohonen maps: Maximal mutual information from enhancing the winner | |
| dc.type | text |