Name
Abstract | ||
|---|---|---|
Self-Organising Maps (SOM) are Artificial Neural Networks used in Pattern Recognition tasks. Their major advantage over other architectures is human readability of a model. However, they often gain poorer accuracy. Mostly used metric in SOM is the Euclidean distance, which is not the best approach to some problems. In this paper, we study an impact of the metric change on the SOM's performance in classification problems. In order to change the metric of the SOM we applied a distance metric learning method, so-called 'Large Margin Nearest Neighbour'. It computes the Mahalanobis matrix, which assures small distance between nearest neighbour points from the same class and separation of points belonging to different classes by large margin. Results are presented on several real data sets, containing for example recognition of written digits, spoken letters or faces. |
Year | DOI | Venue |
|---|---|---|
2012 | 10.1007/978-3-642-29347-4_20 | international conference on artificial intelligence and soft computing |
Keywords | DocType | Volume |
nearest neighbour,improving performance,self-organising map,euclidean distance,metric change,artificial neural networks,mahalanobis matrix,pattern recognition task,large margin,self-organising maps,small distance,distance metric learning method,lmnn,mahalanobis distance,classification | Conference | abs/1407.1201 |
ISSN | Citations | PageRank |
Artificial Intelligence and Soft Computing, Lecture Notes in
Computer Science Volume 7267, 2012, pp 169-177 | 4 | 0.41 |
References | Authors | |
11 | 2 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Piotr Plonski | 1 | 9 | 1.84 |
| Krzysztof Zaremba | 2 | 9 | 1.84 |