Name
Abstract | ||
|---|---|---|
Information theoretical measures are used to design, from first principles, an objective function that can drive a learning machine process to a solution that is robust to perturbations in parameters. Full analytic derivations are given and tested with computational examples showing that indeed the procedure is successful. The final solution, implemented by a robust learning machine, expresses a balance between Shannon differential entropy and Fisher information. This is also surprising in being an analytical relation, given the purely numerical operations of the learning machine. |
Year | DOI | Venue |
|---|---|---|
2016 | 10.3390/e18080295 | ENTROPY |
Keywords | Field | DocType |
information theoretical learning,Shannon entropy,Kullback-Leibler divergence,relative entropy,cross-entropy,Fisher information,relative information | Transfer entropy,Mathematical optimization,Rényi entropy,Information diagram,Shannon's source coding theorem,Joint entropy,Computational learning theory,Statistics,Entropy (information theory),Kullback–Leibler divergence,Mathematics | Journal |
Volume | Issue | Citations |
18 | 8 | 1 |
PageRank | References | Authors |
0.35 | 9 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Pablo Zegers | 1 | 10 | 2.18 |
| B. Roy Frieden | 2 | 16 | 3.22 |
| Carlos Alarcón | 3 | 3 | 0.76 |
| Alexis Fuentes | 4 | 1 | 0.35 |