Name
Abstract | ||
|---|---|---|
Problem specific network structure optimization subsumes the problem of input selection and network topology identification. Requirements to the network should be accuracy and good generalization abilities. In this contribution we describe in detail an evolutionary algorithm which performs both tasks well. Furthermore, approximation results on mathematical and real world data are presented. In this case we used lattice-based associative memory networks (LB-AMNs) using B-splines as basis functions. The method here is not restricted to B-splines as basis functions. The proposed method and algorithm can be seen as optimized classification system. |
Year | DOI | Venue |
|---|---|---|
2001 | 10.1016/S0020-0255(01)00142-6 | Inf. Sci. |
Keywords | Field | DocType |
evolutionary algorithm,lattice-based associative memory network,network topology,feature selection,b spline,associative memory,genetic algorithm,classification system,evolutionary strategy | Content-addressable memory,Feature selection,Evolutionary algorithm,Computer science,Theoretical computer science,Network topology,Evolution strategy,Basis function,Artificial intelligence,Evolutionary programming,Genetic algorithm,Machine learning | Journal |
Volume | Issue | ISSN |
136 | 1-4 | 0020-0255 |
Citations | PageRank | References |
2 | 0.44 | 1 |
Authors | ||
2 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Ingo Renners | 1 | 7 | 3.03 |
| Adolf Grauel | 2 | 34 | 9.56 |