Name
Title | ||
|---|---|---|
Functional network topology learning and sensitivity analysis based on ANOVA decomposition. |
Abstract | ||
|---|---|---|
A new methodology for learning the topology of a functional network from data, based on the ANOVA decomposition technique, is presented. The method determines sensitivity (importance) indices that allow a decision to be made as to which set of interactions among variables is relevant and which is irrelevant to the problem under study. This immediately suggests the network topology to be used in a given problem. Moreover, local sensitivities to small changes in the data can be easily calculated. In this way, the dual optimization problem gives the local sensitivities. The methods are illustrated by their application to artificial and real examples. |
Year | DOI | Venue |
|---|---|---|
2007 | 10.1162/neco.2007.19.1.231 | Neural Computation |
Keywords | Field | DocType |
anova decomposition technique,dual optimization problem,small change,network topology,new methodology,functional network topology,real example,local sensitivity,functional network,sensitivity analysis,optimization problem | Computer science,Models of neural computation,Algorithm,Decomposition method (constraint satisfaction),Network topology,Artificial intelligence,Artificial neural network,Optimization problem,Machine learning | Journal |
Volume | Issue | ISSN |
19 | 1 | 0899-7667 |
Citations | PageRank | References |
7 | 0.73 | 5 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Enrique Castillo | 1 | 555 | 59.86 |
| Noelia Sánchez-Maroño | 2 | 406 | 25.39 |
| Amparo Alonso-Betanzos | 3 | 885 | 76.98 |
| Carmen Castillo | 4 | 57 | 7.43 |