Name
Abstract | ||
|---|---|---|
We investigate the complexity of logistic regression models, which is defined by counting the number of indistinguishable distributions that the model can represent (Balasubramanian, 1997). We find that the complexity of logistic models with binary inputs depends not only on the number of parameters but also on the distribution of inputs in a nontrivial way that standard treatments of complexity d... |
Year | DOI | Venue |
|---|---|---|
2019 | 10.1162/neco_a_01207 | Neural Computation |
Field | DocType | Volume |
Artificial intelligence,Logistic regression,Machine learning,Mathematics | Journal | 31 |
Issue | ISSN | Citations |
8 | 0899-7667 | 1 |
PageRank | References | Authors |
0.35 | 6 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Nicola Bulso | 1 | 1 | 0.35 |
| Matteo Marsili | 2 | 149 | 17.65 |
| Yasser Roudi | 3 | 8 | 2.22 |