Name
Title | ||
|---|---|---|
Self-growing learning vector quantization with additional learning and rule extraction abilities |
Abstract | ||
|---|---|---|
In this paper, we propose a self-growing learning vector quantization (SGLVQ). The proposed SGLVQ is constructed based on the self-organizing map (SOM) and the learning vector quantization (LVQ). Learning of the SGLVQ consists of 3 steps: SOM step, LVQ step, and rule extraction step. In the LVQ step, neurons are incremented and the size of the network is adjusted automatically. The incrementation of neurons enables additional learning and contributes to obtain high recognition ability. In the rule extraction step, rules can be extracted. Computer experiments show the improvement of the recognition rate, the ability of additional learning and extraction of the rules. |
Year | DOI | Venue |
|---|---|---|
2000 | 10.1109/ICSMC.2000.884439 | IEEE International Conference on Systems Man and Cybernetics Conference Proceedings |
Keywords | Field | DocType |
self-organizing map,lerning vector quantization,incrementation of neuron,rule extraction | Computer experiment,Semi-supervised learning,Character recognition,Pattern recognition,Computer science,Learning vector quantization,Handwriting recognition,Self-organizing map,Vector quantization,Artificial intelligence,Artificial neural network,Machine learning | Conference |
ISSN | Citations | PageRank |
1062-922X | 0 | 0.34 |
References | Authors | |
4 | 2 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Dan Mikami | 1 | 118 | 17.60 |
| masafumi hagiwara | 2 | 381 | 65.63 |