Name
Abstract | ||
|---|---|---|
Linear regression problem deal with the solution of an over determined set of linear equations under different assumptions about noise in the experimental data. Some parameterized techniques, designed for a compact treatment of these problems, are briefly reviewed. Then, the GeTLS method is introduced and applied, as learning law, to a novel linear neuron, GeTLS EXIN. Some numerical considerations follow, together with software examples. This neuron yields very good results, especially for large systems of equations. |
Year | DOI | Venue |
|---|---|---|
2000 | 10.1109/IJCNN.2000.859410 | IJCNN (6) |
Keywords | Field | DocType |
getls method,getls exin,novel linear neuron,compact treatment,linear equation,linear regression,different assumption,neuron yield,experimental data,linear regression problem deal,getls exin neuron,good result,system of equations,vectors,sampling methods,cost function,neural nets,linear equations,parameter estimation,statistical analysis,differential equations,least squares approximation | Overdetermined system,Principal component regression,System of linear equations,Computer science,Linear model,Polynomial regression,Proper linear model,Artificial intelligence,Linear predictor function,Linear least squares,Machine learning | Conference |
ISSN | ISBN | Citations |
2161-4393 | 0-7695-0619-4 | 2 |
PageRank | References | Authors |
0.57 | 2 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Giansalvo Cirrincione | 1 | 121 | 13.13 |
| Maurizio Cirrincione | 2 | 124 | 16.58 |
| S. Van Huffel | 3 | 260 | 32.75 |