Name
Abstract | ||
|---|---|---|
The successful development of an application using the multilayer perceptron (MLP) model greatly depends on the structural complexity of the domains involved. Different mathematical and/or statistical techniques can be used to subtract the maximum amount of information of this type from an available sample of the input space. In the context of the MLP model, it has been used to decide on the form the parameters of the network and/or related learning algorithm (LA) should have. This paper describes the information subsumed in the Delaunay triangulation (DT) and Voronoi diagram (VD) of the points comprising the input space of an application, how it might be used to evaluate the convenience of building a network based on the MLP model for its implementation and to estimate an initial architecture that can be subsequently improved by a pruning process. |
Year | DOI | Venue |
|---|---|---|
1995 | 10.1007/3-540-61988-7_22 | IEEE/Nagoya-University World Wisepersons Workshop |
Keywords | Field | DocType |
delaunay triangulations,multi-layer perceptron design,relational learning,multilayer perceptron,voronoi diagram,multi layer perceptron,structural complexity,delaunay triangulation | Bowyer–Watson algorithm,Structural complexity,Theoretical computer science,Multilayer perceptron,Voronoi diagram,Mathematics,Delaunay triangulation | Conference |
ISBN | Citations | PageRank |
3-540-61988-7 | 0 | 0.34 |
References | Authors | |
5 | 3 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Elena Pérez-miñana | 1 | 129 | 6.74 |
| Peter Ross | 2 | 41 | 14.14 |
| John Hallam | 3 | 26 | 7.82 |