Name
Abstract | ||
|---|---|---|
Functional networks are used to determine the optimal transformations to be applied to the response and the predictor variables in linear regression. The main steps required to build the functional network: selection of the initial topology, simplification of the initial functional network, uniqueness of representation, and learning the parameters are discussed, and illustrated with some examples. |
Year | DOI | Venue |
|---|---|---|
2001 | 10.1007/3-540-45720-8_36 | Lecture Notes in Computer Science |
Keywords | Field | DocType |
functional networks,initial functional network,main step,initial topology,optimal transformations,predictor variable,linear regression,functional network,multiple linear regression,optimal transformation,simplification,regression model,regression analysis,topology,learning artificial intelligence,linear transformation | Uniqueness,Regression analysis,Computer science,Minimum description length,Functional networks,Algorithm,Initial topology,Linear map,Linear regression | Conference |
ISBN | Citations | PageRank |
3-540-42235-8 | 5 | 0.59 |
References | Authors | |
3 | 3 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Enrique Castillo | 1 | 555 | 59.86 |
| Ali S. Hadi | 2 | 140 | 15.04 |
| Beatriz Lacruz | 3 | 61 | 6.80 |