Name
Abstract | ||
|---|---|---|
One of the most interesting features of self-organizing maps is the neighbourhood structure between classes highlighted by this technique. The aim of this paper is to present a stochastic method based on bootstrap process for increasing the reliability of the induced neighbourhood structure. The robustness under interest here concerns the sensitivities of the output to the sampling method and to some of the learning options (the initialisation and the order of data presentation). The presented method consists in selecting one map between a group of several solutions resulting from the same self-organizing map algorithm but with various inputs. The selected (robust) map, called R-map, can be perceived as the map, among the group, that corresponds to the most common interpretation of the data set structure. |
Year | DOI | Venue |
|---|---|---|
2005 | 10.1007/11550822_68 | ICANN (1) |
Keywords | Field | DocType |
data presentation,self-organizing map algorithm,common interpretation,stochastic method,bootstrap process,self-organizing map,interesting feature,sampling method,neighbourhood structure,induced neighbourhood structure,sampling methods | Data structure,Pattern recognition,Computer science,Bootstrapping,Robustness (computer science),Neighbourhood (mathematics),Sampling (statistics),Artificial intelligence,Artificial neural network,Bootstrapping (electronics),Difference-map algorithm,Machine learning | Conference |
Volume | ISSN | ISBN |
3696 | 0302-9743 | 3-540-28752-3 |
Citations | PageRank | References |
0 | 0.34 | 3 |
Authors | ||
2 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Patrick Rousset | 1 | 17 | 2.67 |
| Bertrand Maillet | 2 | 47 | 7.45 |