Name
Abstract | ||
|---|---|---|
The self-organizing map has shown to be a stable neural network model for high-dimensional data analysis. However, its applicability is limited by the fact that some knowledge about the data is required to determine the size of the network. In this paper we present the Growing Hierarchical SOM. This dynamically growing architecture evolves into a hierarchical structure of self-organizing maps according to the characteristics of the input data. Furthermore, each map is expanded until it... |
Year | Venue | Keywords |
|---|---|---|
2000 | ESANN | high dimensional data,neural network model |
Field | DocType | Citations |
Document classification,Architecture,Computer science,Self-organizing map,Artificial intelligence,Granularity,Artificial neural network,Machine learning | Conference | 14 |
PageRank | References | Authors |
1.37 | 4 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Michael Dittenbach | 1 | 297 | 26.48 |
| Dieter Merkl | 2 | 846 | 115.65 |
| Andreas Rauber | 3 | 1925 | 216.21 |