Name
Abstract | ||
|---|---|---|
This paper focuses on the possibility of enabling vector quantization learning techniques into dynamic neural fields, as an attempt to enrich their usage in bio-inspired applications. As mathematical approaches prove rather difficult to propose a practical solution, due to the non-linear character of the field equations, we adopt adifferent perspective in order to deal with this problem. This consists in simulating the evolution of the field and design an empirical method able to measure its quality. The developed benchmark framework implementing this approach is used to check whether a given field is capable to behave as expected in various situations, in particular those involving self-organization by vector quantization. |
Year | DOI | Venue |
|---|---|---|
2008 | 10.1109/ICMLA.2008.21 | ICMLA |
Keywords | Field | DocType |
mathematical approach,bio-inspired application,vector quantization,dynamic neural field,empirical method,benchmark framework,enabling vector quantization,field equation,non-linear character,adifferent perspective,neural fields suitable,prototypes,learning artificial intelligence,kernel,data handling,self organization,mathematical model,computational modeling | Kernel (linear algebra),Distance measurement,Pattern recognition,Computer science,Neural fields,Vector quantisation,Theoretical computer science,Field equation,Vector quantization,Artificial intelligence,Group method of data handling,Machine learning | Conference |
Citations | PageRank | References |
2 | 0.43 | 8 |
Authors | ||
2 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Lucian Alecu | 1 | 10 | 2.04 |
| Hervé Frezza-Buet | 2 | 80 | 10.20 |