Name
Abstract | ||
|---|---|---|
In this paper we present a neural network for nonmetric multidimensional scaling. In our approach, the monotone transformation that is a part of every nonmetric scaling algorithm is performed by a special feedforward neural network with a modified backpropagation algorithm. Contrary to traditional methods, we thus explicitly model the monotone transformation by a special purpose neural network. The architecture of the new network and the derivation of the learning rule are given, as well as some experimental results. The experimental results are positive. |
Year | DOI | Venue |
|---|---|---|
2001 | 10.1007/3-540-44816-0_15 | IDA |
Keywords | Field | DocType |
traditional method,modified backpropagation algorithm,special feedforward neural network,nonmetric multidimensional scaling,nonmetric scaling algorithm,neural networks,new network,neural network,monotone transformation,special purpose neural network,backpropagation algorithm,feedforward neural network | Feedforward neural network,Multidimensional scaling,Computer science,Multidimensional analysis,Algorithm,Probabilistic neural network,Learning rule,Artificial intelligence,Backpropagation,Artificial neural network,Monotone polygon,Machine learning | Conference |
ISBN | Citations | PageRank |
3-540-42581-0 | 1 | 0.47 |
References | Authors | |
3 | 4 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Michiel C. van Wezel | 1 | 36 | 4.28 |
| Walter A. Kosters | 2 | 310 | 32.97 |
| Peter Van Der Putten | 3 | 72 | 8.84 |
| Joost N. Kok | 4 | 1429 | 121.49 |