Name
Abstract | ||
|---|---|---|
We present a set of algorithmic complexity estimates. We derive a normalized semi-distance that is shown to outperform the state-of-the-art. We also propose estimators for causality inference on directed acyclic graphs. Illustrative applications include clustering of human writing systems and causality assessment on novel drafts. |
Year | Venue | Field |
|---|---|---|
2017 | European Signal Processing Conference | Causality,Normalization (statistics),Inference,Theoretical computer science,Directed acyclic graph,Artificial intelligence,Cluster analysis,Algorithmic inference,Machine learning,Mathematics,Estimator,Encoding (memory) |
DocType | ISSN | Citations |
Conference | 2076-1465 | 0 |
PageRank | References | Authors |
0.34 | 3 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Marion Revolle | 1 | 0 | 0.34 |
| Francois Cayre | 2 | 244 | 18.86 |
| Nicolas Le Bihan | 3 | 254 | 23.35 |