Name
Abstract | ||
|---|---|---|
TurSOM [1] is a novel self-organizing map algorithm with the capability of connection reorganization, not just neuron reorganization. This behavior facilitates the ability to map distinct patterns in a given input space. Multiple networks exist, and operate independently. This work presents an application driven approach, based on the theoretical and empirical work of previous TurSOM experiments. TurSOM is a highly robust algorithm, designed to eliminate the need for post processing methods of cluster identification using SOM algorithms. One of the applications TurSOM is suitable for, but obviously not limited to, is image segmentation, as it is demonstrated in this work. |
Year | DOI | Venue |
|---|---|---|
2009 | 10.1109/IJCNN.2009.5178750 | IJCNN |
Keywords | Field | DocType |
distinct pattern,applications tursom,empirical work,cluster identification,map algorithm,neuron reorganization,robust algorithm,previous tursom experiment,som algorithm,connection reorganization,magnetic resonance imaging,convergence,clustering algorithms,algorithm design,artificial neural networks,data mining,pattern recognition,information science,resonance,robustness,algorithm design and analysis,image segmentation,neural networks | Convergence (routing),Data mining,Pattern recognition,Pattern clustering,Computer science,Image segmentation,Artificial intelligence,Artificial neural network,Cluster analysis,Difference-map algorithm,Machine learning | Conference |
ISSN | Citations | PageRank |
2161-4393 | 3 | 0.50 |
References | Authors | |
11 | 3 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Derek Beaton | 1 | 47 | 5.52 |
| Iren Valova | 2 | 136 | 25.44 |
| Daniel MacLean | 3 | 113 | 11.82 |